COLING 2022

• German
• Italian
• Chinese
• Japanese

TLDRs

• Do Language Models Make Human-like Predictions about the Coreferents of Italian Anaphoric Zero Pronouns?
  • James A. Michaelov, Benjamin K. Bergen
  • TLDR: We show that language models can capture human behavior when exposed to sentences with zero pronouns.
• Language Acquisition through Intention Reading and Pattern Finding
  • Jens Nevens, Jonas Doumen, Paul Van Eecke, Katrien Beuls
  • TLDR: We present a mechanistic model of the intention reading process and its integration with pattern finding capacities in child language acquisition.
• Stability of Syntactic Dialect Classification over Space and Time
  • Jonathan Dunn, Sidney Wong
  • TLDR: We show that dialect classifiers based on syntactic representations can change over time and show that the grammar of a dialect is internally heterogeneous.
• Subject Verb Agreement Error Patterns in Meaningless Sentences: Humans vs. BERT
  • Karim Lasri, Olga Seminck, Alessandro Lenci, Thierry Poibeau
  • TLDR: We show that meaningfulness interferes with subject verb number agreement in English in syntactic structures of various complexities.
• Measuring Morphological Fusion Using Partial Information Decomposition
  • Michaela Socolof, Jacob Louis Hoover, Richard Futrell, Alessandro Sordoni, Timothy J. O’Donnell
  • TLDR: We provide a mathematically precise way of characterizing morphological systems using partial information decomposition, a framework for decomposing mutual information into three components: unique, redundant, and synergistic information.
• Smells like Teen Spirit: An Exploration of Sensorial Style in Literary Genres
  • Osama Khalid, Padmini Srinivasan
  • TLDR: We explore the role of sensorial language in writing and show that it is not random, but rather a choice that is likely involved.
• Metaphorical Polysemy Detection: Conventional Metaphor Meets Word Sense Disambiguation
  • Rowan Hall Maudslay, Simone Teufel
  • TLDR: We present a novel method for detecting conventional metaphors in English WordNet, which outperforms a state-of-the-art model on both ROC-AUC and ROC scores.
• Machine Reading, Fast and Slow: When Do Models “Understand” Language?
  • Sagnik Ray Choudhury, Anna Rogers, Isabelle Augenstein
  • TLDR: We investigate the behavior of reading comprehension models with respect to two linguistic ”skills”: coreference resolution and comparison.
• Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings
  • Sooji Han, Rui Mao, Erik Cambria
  • TLDR: We propose a novel explainable model for automatic diagnosis of depression on Twitter using metaphor concept mappings.
• Multi-view and Cross-view Brain Decoding
  • Subba Reddy Oota, Jashn Arora, Manish Gupta, Raju S. Bapi
  • TLDR: We propose two novel brain decoding setups for multi-view decoding and show that models trained on picture or sentence view of stimuli are better MV decoders than models trained in word cloud view.
• Visio-Linguistic Brain Encoding
  • Subba Reddy Oota, Jashn Arora, Vijay Rowtula, Manish Gupta, Raju S. Bapi
  • TLDR: We propose VisualBERT, a multi-modal Transformer for brain encoding, which significantly outperforms existing multi-mode CNNs and image Transformers.
• Gestures Are Used Rationally: Information Theoretic Evidence from Neural Sequential Models
  • Yang Xu, Yang Cheng, Riya Bhatia
  • TLDR: We study the degree to which gestures can effectively transmit information in verbal channel.
• Revisiting Statistical Laws of Semantic Shift in Romance Cognates
  • Yoshifumi Kawasaki, Maëlys Salingre, Marzena Karpinska, Hiroya Takamura, Ryo Nagata
  • TLDR: We show that frequency and polysemy are correlated with lexical semantic shift in Romance cognates.
• Character Jacobian: Modeling Chinese Character Meanings with Deep Learning Model
  • Yu-Hsiang Tseng, Shu-Kai Hsieh
  • TLDR: We propose a novel word-formation process model and character Jacobians that can help to explain the word-formal properties of compound words.
• COMMA: Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities
  • Yuqiang Xie, Yue Hu, Wei Peng, Guanqun Bi, Luxi Xing
  • TLDR: We present the first study that investigates the viability of modeling motivations, emotions, and actions in language-based human activities.
• Exploring Semantic Spaces for Detecting Clustering and Switching in Verbal Fluency
  • Özge Alacam, Simeon Schüz, Martin Wegrzyn, Johanna Kißler, Sina Zarrieß
  • TLDR: We analyze the fitness of various word/concept representations in analyzing verbal fluency sequences and show that ConceptNet embeddings are more suitable for detecting clusters and switches within and across different categories.
• Neuro-Symbolic Visual Dialog
  • Adnen Abdessaied, Mihai Bâce, Andreas Bulling
  • TLDR: We propose Neuro-Symbolic Visual Dialog, a novel method to combine deep learning and symbolic program execution for multi-round visually-grounded reasoning.
• LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging
  • Andy Rosenbaum, Saleh Soltan, Wael Hamza, Yannick Versley, Markus Boese
  • TLDR: We present LINGUIST, a method for generating annotated data for Intent Classification and Slot Tagging (IC+ST), via fine-tuning AlexaTM 5B, a 5-billion-parameter multilingual sequence-to-sequence (seq2seq) model, on a flexible instruction prompt.
• Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning
  • Atsumoto Ohashi, Ryuichiro Higashinaka
  • TLDR: We propose ANTOR, a method for language generation in dialogue systems that learns to generate natural utterances adapted to the dialogue environment.
• TAKE: Topic-shift Aware Knowledge sElection for Dialogue Generation
  • Chenxu Yang, Zheng Lin, Jiangnan Li, Fandong Meng, Weiping Wang, Lanrui Wang, Jie Zhou
  • TLDR: We propose Topic-shift Aware Knowledge sElector, a method for knowledge-grounded dialogue generation that learns pseudo labels and addresses the noise problem in pseudo labels.
• Dynamic Dialogue Policy for Continual Reinforcement Learning
  • Christian Geishauser, Carel van Niekerk, Hsien-chin Lin, Nurul Lubis, Michael Heck, Shutong Feng, Milica Gašić
  • TLDR: We propose a dynamic dialogue policy transformer for continual reinforcement learning and a continual learning algorithm for dialogue policy optimisation.
• GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution
  • Danfeng Guo, Arpit Gupta, Sanchit Agarwal, Jiun-Yu Kao, Shuyang Gao, Arijit Biswas, Chien-Wei Lin, Tagyoung Chung, Mohit Bansal
  • TLDR: We propose a unified multimodal coreference resolution framework for multi-turn dialogues with scene images.
• Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement
  • Dongshi Ju, Shi Feng, Pengcheng Lv, Daling Wang, Yifei Zhang
  • TLDR: We propose a graph convolution network model for multi-party personalized dialogue dataset and propose a method for addressee selecting mechanism that integrates personas, dialogue utterances, and external text knowledge in a unified graph.
• Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning
  • Geunseob Oh, Rahul Goel, Chris Hidey, Shachi Paul, Aditya Gupta, Pararth Shah, Rushin Shah
  • TLDR: We propose a novel NAR semantic parser that introduces intent conditioning on the decoder.
• Autoregressive Entity Generation for End-to-End Task-Oriented Dialog
  • Guanhuan Huang, Xiaojun Quan, Qifan Wang
  • TLDR: We propose to generate the entity autoregressively before leveraging it to guide the response generation in an end-to-end system.
• Continual Few-shot Intent Detection
  • Guodun Li, Yuchen Zhai, Qianglong Chen, Xing Gao, Ji Zhang, Yin Zhang
  • TLDR: We propose a novel method for continuous learning of intent detection that can prevent catastrophic forgetting and positive knowledge transfer across tasks.
• “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations
  • Henning Wachsmuth, Milad Alshomary
  • TLDR: We present a first corpus of dialogical explanations to enable NLP research on how humans explain as well as on how AI can learn to imitate this process.
• Schema Encoding for Transferable Dialogue State Tracking
  • Hyunmin Jeon, Gary Geunbae Lee
  • TLDR: We propose a new method for transferable dialogue state tracking using schemas and neural models.
• A Personalized Dialogue Generator with Implicit User Persona Detection
  • Itsugun Cho, Dongyang Wang, Ryota Takahashi, Hiroaki Saito
  • TLDR: We propose a novel personalized dialogue generator by detecting an implicit user persona.
• Incorporating Casual Analysis into Diversified and Logical Response Generation
  • Jiayi Liu, Wei Wei, Zhixuan Chu, Xing Gao, Ji Zhang, Tan Yan, Yulin Kang
  • TLDR: We propose to predict the mediators in dialogues and use them to generate relevant and informative responses.
• Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations
  • Jiazhan Feng, Chongyang Tao, Zhen Li, Chang Liu, Tao Shen, Dongyan Zhao
  • TLDR: We propose a reciprocal learning approach to jointly optimize a knowledge retriever and a response ranker for knowledge-grounded response retrieval without ground-truth knowledge labels.
• CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling
  • Jinfeng Zhou, Bo Wang, Zhitong Yang, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou
  • TLDR: We propose CR-GIS with a parallel star framework to model the goal-aware implicit user interest sequence in a dialog.
• GRASP: Guiding Model with RelAtional Semantics Using Prompt for Dialogue Relation Extraction
  • Junyoung Son, Jinsung Kim, Jungwoo Lim, Heuiseok Lim
  • TLDR: We propose a Guiding model with RelAtional Semantics using Prompt to capture relational semantic clues of dialogue and use them to improve the dialogue-based relation extraction task.
• PEPDS: A Polite and Empathetic Persuasive Dialogue System for Charity Donation
  • Kshitij Mishra, Azlaan Mustafa Samad, Palak Totala, Asif Ekbal
  • TLDR: We propose a polite, empathetic persuasive dialogue system that improves the rate of persuasive responses with emotion and politeness acknowledgement compared to the current state-of-the-art dialogue models, while also enhancing the dialogue’s engagement and maintaining the linguistic quality.
• DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling
  • Lahari Poddar, Peiyao Wang, Julia Reinspach
  • TLDR: We propose a novel data augmentation method for dialogue context augmentation that improves the robustness of dialogue representation and improves the performance of existing dialogue augmentation methods.
• A Closer Look at Few-Shot Out-of-Distribution Intent Detection
  • Li-Ming Zhan, Haowen Liang, Lu Fan, Xiao-Ming Wu, Albert Y.S. Lam
  • TLDR: We show that existing OOD intent detection methods are not adequate in dealing with this problem.
• CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue
  • Libo Qin, Qiguang Chen, Tianbao Xie, Qian Liu, Shijue Huang, Wanxiang Che, Zhou Yu
  • TLDR: We propose a novel approach for consistent identification in task-oriented dialogs by introducing an explicit interaction paradigm, Cycle Guided Interactive learning Model (CGIM), which achieves to make information exchange explicitly from all the three tasks.
• CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations
  • Lin Xu, Qixian Zhou, Jinlan Fu, Min-Yen Kan, See-Kiong Ng
  • TLDR: We propose a novel multi-document co-referential graph for document-grounded dialogs that can be used to model inter- and intra-document knowledge relations for dialog flows.
• SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation
  • Longxuan Ma, Ziyu Zhuang, Weinan Zhang, Mingda Li, Ting Liu
  • TLDR: We propose a novel automatic dialogue evaluation framework that can automatically assign fine-grained scores for arbitrarily dialogue data.
• Open-Domain Dialog Evaluation Using Follow-Ups Likelihood
  • Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann, Walter Daelemans
  • TLDR: We present a new automated evaluation method based on the use of follow-ups.
• Joint Goal Segmentation and Goal Success Prediction on Multi-Domain Conversations
  • Meiguo Wang, Benjamin Yao, Bin Guo, Xiaohu Liu, Yu Zhang, Tuan-Hung Pham, Chenlei Guo
  • TLDR: We propose a novel automatic dialogue evaluation framework that jointly performs two tasks: goal segmentation and goal success prediction.
• Slot Dependency Modeling for Zero-Shot Cross-Domain Dialogue State Tracking
  • Qingyue Wang, Yanan Cao, Piji Li, Yanhe Fu, Zheng Lin, Li Guo
  • TLDR: We propose a new method for zero-shot dialogue state tracking that captures the slot dependencies and enables generalization across unseen domains.
• Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language Model
  • Sixing Wu, Ying Li, Ping Xue, Dawei Zhang, Zhonghai Wu
  • TLDR: We propose a novel two-stage framework SAKDP for knowledge-grounded dialogue generation that uses a ranking network PriorRanking to estimate relevance of a retrieved knowledge fact and a section-aware strategy to encode the linearized knowledge.
• Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection
  • Souvik Das, Sougata Saha, Rohini K. Srihari
  • TLDR: We propose a suite of fusion strategies that capture the interaction between persona, emotion, and entailment information of the utterances of the user and train a BERT-based response selection system that outperforms the previous methods by margins larger than 2.3% on original personas and 1.9% on revised personas in terms of hits@1 accuracy.
• A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging
  • Stefano Mezza, Wayne Wobcke, Alan Blair
  • TLDR: We propose a neural architecture for dialogue act tagging that uses syntactic information from previous turns and semantic information from dialogue interactions.
• SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding
  • Wanwei He, Yinpei Dai, Binyuan Hui, Min Yang, Zheng Cao, Jianbo Dong, Fei Huang, Luo Si, Yongbin Li
  • TLDR: We propose a tree-structured pre-trained conversation model that learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised contrastive pre-training.
• ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension
  • Xiao Zhang, Heyan Huang, Zewen Chi, Xian-Ling Mao
  • TLDR: We propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning T5 (ET5).
• CoHS-CQG: Context and History Selection for Conversational Question Generation
  • Xuan Long Do, Bowei Zou, Liangming Pan, Nancy F. Chen, Shafiq Joty, Ai Ti Aw
  • TLDR: We propose a novel CoHS module for conversational question generation which optimises the context and history of the input and achieves state-of-the-art performances on CoQA in both the answer-aware and answer-unaware settings.
• Semantic-based Pre-training for Dialogue Understanding
  • Xuefeng Bai, Linfeng Song, Yue Zhang
  • TLDR: We propose a semantic-based pre-training framework for dialogue pre-trainers that captures the core semantic information in dialogues during pre- training.
• Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation
  • Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou, Pei Wang, Weiran Xu
  • TLDR: We propose a Bayesian OOD detection framework to calibrate distribution uncertainty using Monte-Carlo Dropout.
• Tracking Satisfaction States for Customer Satisfaction Prediction in E-commerce Service Chatbots
  • Yang Sun, Liangqing Wu, Shuangyong Song, Xiaoguang Yu, Xiaodong He, Guohong Fu
  • TLDR: We propose a dialogue-level classification model named DialogueCSP to track satisfaction states for CSP.
• Towards Multi-label Unknown Intent Detection
  • Yawen Ouyang, Zhen Wu, Xinyu Dai, Shujian Huang, Jiajun Chen
  • TLDR: We propose a new method for multi-class unknown intent detection that is intuitive and effective.
• Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation
  • Yihe Wang, Yitong Li, Yasheng Wang, Fei Mi, Pingyi Zhou, Xin Wang, Jin Liu, Xin Jiang, Qun Liu
  • TLDR: We present a retrieval-generation training framework that can take advantage of the heterogeneous and noisy training data by considering them as evidence.
• MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
  • Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang
  • TLDR: We propose a simple but effective Multilingual learning framework for Zero-shot Dialogue Generation that can effectively transfer knowledge from an English corpus with large-scale training samples to a non-English corpus with zero samples.
• Target-Guided Open-Domain Conversation Planning
  • Yosuke Kishinami, Reina Akama, Shiki Sato, Ryoko Tokuhisa, Jun Suzuki, Kentaro Inui
  • TLDR: We propose a new task for evaluating the goal-oriented conversational planning ability of neural conversational agents.
• Does GPT-3 Generate Empathetic Dialogues? A Novel In-Context Example Selection Method and Automatic Evaluation Metric for Empathetic Dialogue Generation
  • Young-Jun Lee, Chae-Gyun Lim, Ho-Jin Choi
  • TLDR: We propose a new dialogue generative model, GPT-3, which can generate empathetic dialogues through prompt-based in-context learning in both zero-shot and few-shot settings.
• DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis
  • Yuchen Liu, Jinming Zhao, Jingwen Hu, Ruichen Li, Qin Jin
  • TLDR: We propose a novel Dialogue Emotion Interaction Interaction Network, DialogueEIN, to simulate the emotional inertia, emotional stimulus, global and local emotional evolution in dialogues.
• Towards Enhancing Health Coaching Dialogue in Low-Resource Settings
  • Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp, Bing Liu, Ben Gerber, Nikolaos Agadakos, Shweta Yadav
  • TLDR: We propose a modularized health coaching dialogue system that converses with patients, helps them create and accomplish specific goals, and can address their emotions with empathy.
• Generalized Intent Discovery: Learning from Open World Dialogue System
  • Yutao Mou, Keqing He, Yanan Wu, Pei Wang, Jingang Wang, Wei Wu, Yi Huang, Junlan Feng, Weiran Xu
  • TLDR: We propose a new task for generalized intent discovery and propose a framework for it.
• DialMed: A Dataset for Dialogue-based Medication Recommendation
  • Zhenfeng He, Yuqiang Han, Zhenqiu Ouyang, Wei Gao, Hongxu Chen, Guandong Xu, Jian Wu
  • TLDR: We propose a new method to recommend medications with medical dialogues based on the conversations between doctors and patients.
• Speaker Clustering in Textual Dialogue with Pairwise Utterance Relation and Cross-corpus Dialogue Act Supervision
  • Zhihua Su, Qiang Zhou
  • TLDR: We propose a speaker clustering model for textual dialogues, which groups the utterances of a multi-party dialogue without speaker annotations, so that the actual speakers are identical inside each cluster.
• TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph
  • Zhitong Yang, Bo Wang, Jinfeng Zhou, Yue Tan, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou
  • TLDR: We propose a new method for target-oriented dialog on commonsense knowledge graph to flexibly guide the dialog towards a global target.
• Extractive Summarisation for German-language Data: A Text-level Approach with Discourse Features
  • Freya Hewett, Manfred Stede
  • TLDR: We examine the link between facets of Rhetorical Structure Theory (RST) and the selection of content for extractive summarisation, for German-language texts.
• End-to-End Neural Bridging Resolution
  • Hideo Kobayashi, Yufang Hou, Vincent Ng
  • TLDR: We evaluate bridging resolvers in an end-to-end setting, strengthen them with better encoders, and attempt to gain a better understanding of them via perturbation experiments and a manual analysis of their outputs.
• Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences
  • Jad Kabbara, Jackie Chi Kit Cheung
  • TLDR: We investigate the capabilities of transformer models to perform NLI on cases involving presupposition.
• Re-Examining FactBank: Predicting the Author’s Presentation of Factuality
  • John Murzaku, Peter Zeng, Magdalena Markowska, Owen Rambow
  • TLDR: We present a corrected version of a subset of the FactBank data set.
• The Role of Context and Uncertainty in Shallow Discourse Parsing
  • Katherine Atwell, Remi Choi, Junyi Jessy Li, Malihe Alikhani
  • TLDR: We show that adding uncertainty measures to discourse parsing models improves accuracy and calibration, and that incorporating confidence in the model’s temperature function can lead to models with significantly better-calibrated confidence measures.
• Improving Commonsense Contingent Reasoning by Pseudo-data and Its Application to the Related Tasks
  • Kazumasa Omura, Sadao Kurohashi
  • TLDR: We present a novel method for generating pseudo-problems for the reasoning of contingent relations between basic events.
• A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives
  • Qingcheng Zeng, An-Ran Li
  • TLDR: We provide a comprehensive overview of computational irony, insights from linguisic theory and cognitive science, as well as its interactions with downstream NLP tasks and newly proposed multi-X irony processing perspectives.
• Towards Identifying Alternative-Lexicalization Signals of Discourse Relations
  • René Knaebel, Manfred Stede
  • TLDR: We present the first approaches for recognizing alternative lexicalizations in the Penn Discourse Treebank and provide an empirical analysis of their performance.
• Topicalization in Language Models: A Case Study on Japanese
  • Riki Fujihara, Tatsuki Kuribayashi, Kaori Abe, Ryoko Tokuhisa, Kentaro Inui
  • TLDR: We analyze whether neural language models can capture discourse-level preferences in text generation.
• “No, They Did Not”: Dialogue Response Dynamics in Pre-trained Language Models
  • Sanghee J. Kim, Lang Yu, Allyson Ettinger
  • TLDR: We show that language models show strong sensitivity to embedded clauses and ellipsis, but weak sensitivity to at-issueness and ellipse.
• New or Old? Exploring How Pre-Trained Language Models Represent Discourse Entities
  • Sharid Loáiciga, Anne Beyer, David Schlangen
  • TLDR: We investigate the information-status of entities in pre-trained language models and show that they encode information on whether an entity has been introduced before or not in a discourse.
• Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues
  • Sougata Saha, Souvik Das, Rohini K. Srihari
  • TLDR: We present Dialo-AP, an end-to-end argument parser that constructs argument graphs from dialogues.
• ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation Recognition
  • Wei Xiang, Zhenglin Wang, Lu Dai, Bang Wang
  • TLDR: We propose a new paradigm for implicit discourse relation recognition based on implicit connectives and a new prompt for the task.
• A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing
  • Yaxin Fan, Peifeng Li, Fang Kong, Qiaoming Zhu
  • TLDR: We propose a distance-aware multi-task framework DAMT that incorporates the strengths of graph-based and transition-based paradigms to facilitate the graph-by-graph parsing of conversational discourse parsing.
• Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre
  • Arman Kazmi, Sidharth Ranjan, Arpit Sharma, Rajakrishnan Rajkumar
  • TLDR: We use linguistically motivated features to classify paragraph-level text into fiction and non-fiction genre using a logistic regression model and infers lexical and syntactic properties that distinguish the two genres.
• Semantic Sentence Matching via Interacting Syntax Graphs
  • Chen Xu, Jun Xu, Zhenhua Dong, Ji-Rong Wen
  • TLDR: We propose a graph-based method for semantic sentence matching in which each sentence is represented as a directed graph according to its syntactic structures.
• Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network
  • Chong Zhang, He Zhu, Xingyu Peng, Junran Wu, Ke Xu
  • TLDR: We propose a graph neural network based on hierarchical information for text classification.
• SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training
  • Dan Qiao, Chenchen Dai, Yuyang Ding, Juntao Li, Qiang Chen, Wenliang Chen, Min Zhang
  • TLDR: We present a novel method for label noise in text classification that uses a single model to handle label noise and a novel mixup training strategy.
• Community Topic: Topic Model Inference by Consecutive Word Community Discovery
  • Eric Austin, Osmar R. Zaïane, Christine Largeron
  • TLDR: We present a new topic modelling algorithm that produces coherent topics that can be used for downstream applications.
• Where to Attack: A Dynamic Locator Model for Backdoor Attack in Text Classifications
  • Heng-yang Lu, Chenyou Fan, Jun Yang, Cong Hu, Wei Fang, Xiao-jun Wu
  • TLDR: We propose a novel text-based BDA model which can predict Positions-to-Poison (P2P) dynamically without human intervention.
• Locally Distributed Activation Vectors for Guided Feature Attribution
  • Housam K. B. Bashier, Mi-Young Kim, Randy Goebel
  • TLDR: We present a method to learn explanations-specific representations while constructing deep network models for text classification.
• Addressing Leakage in Self-Supervised Contextualized Code Retrieval
  • Johannes Villmow, Viola Campos, Adrian Ulges, Ulrich Schwanecke
  • TLDR: We propose a novel approach to contextualized code retrieval based on mutual identifier masking, dedentation, and the selection of syntax-aligned targets.
• A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal Products
  • Kesong Liu, Jianhui Jiang, Feifei Lyu
  • TLDR: We present a biomedical knowledge enhanced pre-trained language model for medicinal product vertical search.
• CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval
  • Kung-Hsiang Huang, ChengXiang Zhai, Heng Ji
  • TLDR: We present the first cross-lingual retrieval framework for fact-checking in low-resource languages.
• E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models
  • Ling Ge, ChunMing Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, JiHong Liu, Hong Zhang, Wenyi Qin, Richong Zhang
  • TLDR: We propose an enhanced variational word masks approach, named E-VarM, to improve the interpretability and accuracy of text classification models.
• Attribute Injection for Pretrained Language Models: A New Benchmark and an Efficient Method
  • Reinald Kim Amplayo, Kang Min Yoo, Sang-Woo Lee
  • TLDR: We propose a new attribute injection method for neural-based NLP models that uses plug-in feed-forward modules to include attributes independently of or jointly with text.
• Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning
  • Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil
  • TLDR: We propose to improve the out-of-domain generalization of Dense Passage Retrieval (DPR) through synthetic data augmentation only in the source domain.
• Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval
  • Robert Litschko, Ivan Vulić, Goran Glavaš
  • TLDR: We present a modular approach to cross-lingual transfer by training language adapters and reranking adapters on top of multilingual encoders and show that it outperforms standard zero-shot transfer with full MMT fine-tuning.
• LIME: Weakly-Supervised Text Classification without Seeds
  • Seongmin Park, Jihwa Lee
  • TLDR: We present LIME, a framework for weakly-supervised text classification that entirely replaces the brittle seed-word generation process with entailment-based pseudo-classification.
• Multi-Stage Framework with Refinement Based Point Set Registration for Unsupervised Bi-Lingual Word Alignment
  • Silviu Vlad Oprea, Sourav Dutta, Haytham Assem
  • TLDR: We propose BioSpere, a novel multi-stage framework for unsupervised mapping of bi-lingual word embeddings onto a shared vector space, by combining adversarial initialization, refinement procedure and point set registration.
• EM-PERSONA: EMotion-assisted Deep Neural Framework for PERSONAlity Subtyping from Suicide Notes
  • Soumitra Ghosh, Dhirendra Kumar Maurya, Asif Ekbal, Pushpak Bhattacharyya
  • TLDR: We propose a novel EMotion-assisted PERSONAlity Detection Framework for personality subtyping from suicide notes.
• Dense Template Retrieval for Customer Support
  • Tiago Mesquita, Bruno Martins, Mariana Almeida
  • TLDR: We propose a new algorithm for generating and using template-aware queries and templates for customer support scenarios.
• Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification
  • Wei Huang, Chen Liu, Bo Xiao, Yihua Zhao, Zhaoming Pan, Zhimin Zhang, Xinyun Yang, Guiquan Liu
  • TLDR: We propose a novel hierarchical text classification model for HTC that exploits hierarchical dependency across different levels and outperforms all state-of-the-art HTC approaches especially in Macro-F1.
• MuSeCLIR: A Multiple Senses and Cross-lingual Information Retrieval Dataset
  • Wing Yan Li, Julie Weeds, David Weir
  • TLDR: We provide a robust evaluation of cross-lingual information retrieval systems’ disambiguation ability.
• Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification
  • Xu Zhang, Zejie Liu, Yanzheng Xiang, Deyu Zhou
  • TLDR: We propose a novel framework for text classification which implements a two-stage training strategy and a fine-tuning strategy to exploit the knowledge in the pre-trained model.
• Adaptive Feature Discrimination and Denoising for Asymmetric Text Matching
  • Yan Li, Chenliang Li, Junjun Guo
  • TLDR: We propose a novel adaptive feature discrimination and denoising model for asymmetric text matching, called ADDAX.
• Rethinking Data Augmentation in Text-to-text Paradigm
  • Yanan Chen, Yang Liu
  • TLDR: As manually labelling data can be costly, some recent studies tend to augment the training data for improving the generalization power of machine learning models, known as
• ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification
  • Yen-Hao Huang, Yi-Hsin Chen, Yi-Shin Chen
  • TLDR: We propose a novel unified GNN model that learns from both document embeddings and contextual word interactions simultaneously for inductive text classification.
• Virtual Knowledge Graph Construction for Zero-Shot Domain-Specific Document Retrieval
  • Yeon Seonwoo, Seunghyun Yoon, Franck Dernoncourt, Trung Bui, Alice Oh
  • TLDR: We propose a novel domain-specific document retrieval method based on graph representation and a novel ablation method.
• MICO: Selective Search with Mutual Information Co-training
  • Zhanyu Wang, Xiao Zhang, Hyokun Yun, Choon Hui Teo, Trishul Chilimbi
  • TLDR: We propose MICO, a new algorithm for efficient and efficient selective search.
• DPTDR: Deep Prompt Tuning for Dense Passage Retrieval
  • Zhengyang Tang, Benyou Wang, Ting Yao
  • TLDR: We propose a new approach for dense retrieval that outperforms previous state-of-the-art models on both MS-MARCO and Natural Questions.
• BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification
  • Ziwen Liu, Josep Grau-Bove, Scott Allan Allan Orr
  • TLDR: We propose BERT-Flow-VAE, a new model for Weakly-Supervised Multi-Label Text Classification that can outperform other WSMLTC models in key metrics and achieve 84% performance of a fully-supervised model.
• Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender
  • Anne Lauscher, Archie Crowley, Dirk Hovy
  • TLDR: We present a set of 5 desiderata for modeling 3rd person pronouns in language technology and evaluate existing and novel approaches w.r.t. these desideratas qualitatively and quantitatively.
• Threat Scenarios and Best Practices to Detect Neural Fake News
  • Artidoro Pagnoni, Martin Graciarena, Yulia Tsvetkov
  • TLDR: We present a set of best practices for generating text detection systems that minimizes its worst-case performance under different threat scenarios.
• From Polarity to Intensity: Mining Morality from Semantic Space
  • Chunxu Zhao, Pengyuan Liu, Dong Yu
  • TLDR: We propose MoralScore, a weakly-supervised framework that can automatically measure moral intensity from text.
• SOS: Systematic Offensive Stereotyping Bias in Word Embeddings
  • Fatma Elsafoury, Steve R. Wilson, Stamos Katsigiannis, Naeem Ramzan
  • TLDR: Systematic Offensive stereotyping in word embeddings could lead to associating marginalised groups with hate speech and profanity, which might lead to blocking and silencing those groups, especially on social media platforms.
• Bigger Data or Fairer Data? Augmenting BERT via Active Sampling for Educational Text Classification
  • Lele Sha, Yuheng Li, Dragan Gasevic, Guanliang Chen
  • TLDR: We propose a method to augment BERT to improve prediction fairness of downstream models by inhibiting this awareness.
• Debiasing Word Embeddings with Nonlinear Geometry
  • Lu Cheng, Nayoung Kim, Huan Liu
  • TLDR: We study biases associated with multiple social categories and show that they are not correlated with each other.
• Debiasing Isn’t Enough! – on the Effectiveness of Debiasing MLMs and Their Social Biases in Downstream Tasks
  • Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki
  • TLDR: We study the relationship between task-agnostic intrinsic and task-specific extrinsic social bias evaluation measures for MLMs, and find that there exists only a weak correlation between these two types of evaluation measures.
• Quantifying Bias from Decoding Techniques in Natural Language Generation
  • Mayukh Das, Wolf Tilo Balke
  • TLDR: We present an extensive analysis of bias from decoding techniques for open-domain language generation considering the entire decoding space.
• A Study of Implicit Bias in Pretrained Language Models against People with Disabilities
  • Pranav Narayanan Venkit, Mukund Srinath, Shomir Wilson
  • TLDR: We show that popular PLMs trained on large corpora widely favor ableist language.
• Social Norms-Grounded Machine Ethics in Complex Narrative Situation
  • Tao Shen, Xiubo Geng, Daxin Jiang
  • TLDR: We present a novel norm-supported ethical judgment model in line with neural module networks to alleviate dilemma situations and improve norm-level explainability.
• Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling
  • Thiemo Wambsganss, Vinitra Swamy, Roman Rietsche, Tanja Käser
  • TLDR: We analyze bias in pre-trained German language models and their embedding architectures on a corpus of 9,165 German peer-reviews collected from university students over five years.
• Dynamic Relevance Graph Network for Knowledge-Aware Question Answering
  • Chen Zheng, Parisa Kordjamshidi
  • TLDR: We propose a novel graph neural network architecture based on the question and answers entities and use the relevance scores between the nodes to establish new edges dynamically for learning node representations in the graph network.
• SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification
  • Eunhwan Park, Jong-Hyeon Lee, Jeon Dong Hyeon, Seonhoon Kim, Inho Kang, Seung-Hoon Na
  • TLDR: This study proposes
• Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder
  • Fangyu Lei, Shizhu He, Xiang Li, Jun Zhao, Kang Liu
  • TLDR: We propose a novel relational graph enhanced table-text Numerical reasoning model with Tree decoder and a novel graph modeling method for alignment between questions, tables, and paragraphs.
• Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference
  • Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu
  • TLDR: We propose a novel and effective closed-loop neural-symbolic learning framework EngineKG via incorporating our developed KGE and rule learning modules.
• Table-based Fact Verification with Self-labeled Keypoint Alignment
  • Guangzhen Zhao, Peng Yang
  • TLDR: We propose a novel dual-view alignment module based on the statement and table views to investigate evidence correlation between the statement sentence and table attribute.
• IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification
  • Hao Wang, Yangguang Li, Zhen Huang, Yong Dou
  • TLDR: We propose to integrate multi-view contextual information for fact extraction and verification of Wikipedia documents and achieve state-of-the-art performance on the task.
• Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words
  • Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu
  • TLDR: We propose a simple yet effective approach to help models learn rare biomedical words during tuning with prompt.
• Self-Supervised Intermediate Fine-Tuning of Biomedical Language Models for Interpreting Patient Case Descriptions
  • Israa Alghanmi, Luis Espinosa-Anke, Steven Schockaert
  • TLDR: We propose a new method for improving biomedical language models by improving their ability to interpret case reports and case descriptions from PubMed abstracts.
• Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference
  • Jamell Dacon, Haochen Liu, Jiliang Tang
  • TLDR: We present a new method for augmenting NLP models trained on standard English text with diverse language features and propose two simple debiasing methods for NLP tasks.
• Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop?
  • Jesus Lovon-Melgarejo, Jose G. Moreno, Romaric Besançon, Olivier Ferret, Lynda Tamine
  • TLDR: We propose a set of inference strategies to improve multi-hop reasoning in pre-trained language models.
• Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension
  • Jialin Chen, Zhuosheng Zhang, Hai Zhao
  • TLDR: We propose a holistic graph network for logical reasoning that deals with context at both discourse-level and word-level as the basis for logical inference.
• Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering
  • Jianguo Mao, Jiyuan Zhang, Zengfeng Zeng, Weihua Peng, Wenbin Jiang, Xiangdong Wang, Hong Liu, Yajuan Lyu
  • TLDR: We propose a novel model for biomedical question answering that learns semantics within and among biomedical evidences and performs dynamic reasoning based on the hierarchical representations of evidences.
• ArT: All-round Thinker for Unsupervised Commonsense Question Answering
  • Jiawei Wang, Hai Zhao
  • TLDR: We propose an approach of All-round Thinker (ArT) by fully taking association during knowledge generating.
• Teaching Neural Module Networks to Do Arithmetic
  • Jiayi Chen, Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari
  • TLDR: We propose to improve numerical reasoning capabilities of neural module networks by bridging the gap between its interpreter and the complex questions.
• An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding
  • Jie Cao, Jing Xiao
  • TLDR: We present a new dataset for automatic geometric problem solving based on neural networks and a new module for deep neural text encoder.
• Competence-based Question Generation
  • Jingxuan Tu, Kyeongmin Rim, James Pustejovsky
  • TLDR: We present a method to generate competence-based question generation using English cooking recipes and a dataset of English cooking instructions.
• Coalescing Global and Local Information for Procedural Text Understanding
  • Kaixin Ma, Filip Ilievski, Jonathan Francis, Eric Nyberg, Alessandro Oltramari
  • TLDR: We propose a novel procedural text understanding model that optimizes for both precision and recall.
• Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task
  • Liang Wen, Juan Li, Houfeng Wang, Yingwei Luo, Xiaolin Wang, Xiaodong Zhang, Zhicong Cheng, Dawei Yin
  • TLDR: We present a series of neural models to evaluate the relevancy of answer summaries in WikiHowQA dataset and show that the answer summary is not useful for answer selection task.
• Case-Based Abductive Natural Language Inference
  • Marco Valentino, Mokanarangan Thayaparan, André Freitas
  • TLDR: We propose a new abductive framework for multi-hop natural language inference that addresses unseen inference problems by analogical transfer of prior explanations from similar examples.
• Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph
  • Mingchen Li, Shihao Ji
  • TLDR: We propose a novel Structure-BERT to predict the semantic structure of a question and use it to filter out noisy query graph candidates.
• Repo4QA: Answering Coding Questions via Dense Retrieval on GitHub Repositories
  • Minyu Chen, Guoqiang Li, Chen Ma, Jingyang Li, Hongfei Fu
  • TLDR: We propose a new approach to bridge the semantic gap between Stack Overflow and GitHub repositories by combining supervised contrastive loss and hard negative sampling.
• Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL
  • Octavian Popescu, Irene Manotas, Ngoc Phuoc An Vo, Hangu Yeo, Elahe Khorashani, Vadim Sheinin
  • TLDR: We propose a novel system for Text-to-SQL task that uses rule-based and deep learning to improve performance of unseen databases.
• Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering
  • Priyanka Sen, Alham Fikri Aji, Amir Saffari
  • TLDR: We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models.
• Can Edge Probing Tests Reveal Linguistic Knowledge in QA Models?
  • Sagnik Ray Choudhury, Nikita Bhutani, Isabelle Augenstein
  • TLDR: We show that if an LM is fine-tuned, does the encoding of linguistic information in it change, as measured by EP tests?
• Conversational QA Dataset Generation with Answer Revision
  • Seonjeong Hwang, Gary Geunbae Lee
  • TLDR: We propose a novel framework for conversational question-answer generation that automatically generates a large-scale conversational answer dataset based on input passages.
• DABERT: Dual Attention Enhanced BERT for Semantic Matching
  • Sirui Wang, Di Liang, Jian Song, Yuntao Li, Wei Wu
  • TLDR: We propose a novel Dual Attention Enhanced BERT (DABERT) to enhance the ability of BERT to capture fine-grained differences in sentence pairs.
• Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering
  • Siyuan Wang, Zhongyu Wei, Zhihao Fan, Qi Zhang, Xuanjing Huang
  • TLDR: We propose an interpretable stepwise reasoning framework for multi-hop reasoning that incorporates both single-hop supporting sentence identification and single-hops question generation at each intermediate step, and utilize the inference of the current hop for the next until reasoning out the final result.
• Less Is Better: Recovering Intended-Feature Subspace to Robustify NLU Models
  • Ting Wu, Tao Gui
  • TLDR: We propose a novel model for recovering intended features from highly biased datasets and use them to improve generalization to out-of-distribution set.
• CORN: Co-Reasoning Network for Commonsense Question Answering
  • Xin Guan, Biwei Cao, Qingqing Gao, Zheng Yin, Bo Liu, Jiuxin Cao
  • TLDR: We propose a novel model for commonsense question answering that incorporates contextual text representation and relationships between QA entities in KG, and a novel subgraph construction method for co-reasoning.
• Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases
  • Xixin Hu, Xuan Wu, Yiheng Shu, Yuzhong Qu
  • TLDR: We propose GMT-KBQA, a Generation-based KBQA method via Multi-Task learning, to better retrieve and utilize auxiliary information.
• CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers
  • Yiming Ju, Weikang Wang, Yuanzhe Zhang, Suncong Zheng, Kang Liu, Jun Zhao
  • TLDR: We propose a new task for conditional question answering with hierarchical multi-span answers, where both the hierarchical relations and the conditions need to be extracted.
• To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.
  • Yitian Li, Jidong Tian, Wenqing Chen, Caoyun Fan, Hao He, Yaohui Jin
  • TLDR: We propose a systematic method to diagnose the correlations between an NLU dataset and a specific NLU skill, and then take a fundamental reasoning skill, logical reasoning, as an example for analysis.
• ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering
  • Yu Gu, Yu Su
  • TLDR: We present a novel generation-based model for question answering on knowledge bases that addresses both the large search space and the schema linking challenges in a unified framework with two mutually boosting ingredients: dynamic program induction for tackling the large query search space, and dynamic contextualized encoding for schema linking.
• Unsupervised Question Answering via Answer Diversifying
  • Yuxiang Nie, Heyan Huang, Zewen Chi, Xian-Ling Mao
  • TLDR: We propose a novel unsupervised question answering method by diversifying answers, named DiverseQA.
• Weakly Supervised Formula Learner for Solving Mathematical Problems
  • Yuxuan Wu, Hideki Nakayama
  • TLDR: Weakly Supervised Formula Learner is a learning framework that enables models to learn optimal formulas autonomously.
• Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing
  • Zeyi Zhong, Min Yang, Ruifeng Xu
  • TLDR: We propose a novel Spurious Correlation reduction method to improve the robustness of the neural ANswer selection models (SCAN) from the sample and feature perspectives by removing the feature dependencies and language biases in answer selection.
• Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering
  • Zhengbao Jiang, Jun Araki, Haibo Ding, Graham Neubig
  • TLDR: We show that generative question answering models lack zero-shot multi-hop reasoning ability, but can be encouraged by advances in training or modeling techniques.
• Domain Adaptation for Question Answering via Question Classification
  • Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang
  • TLDR: We propose a novel framework for domain adaptation in question answering using question classification and self-supervised adaptation.
• Prompt-based Conservation Learning for Multi-hop Question Answering
  • Zhenyun Deng, Yonghua Zhu, Yang Chen, Qianqian Qi, Michael Witbrock, Patricia Riddle
  • TLDR: We propose Prompt-based Conservation Learning for multi-hop question answering, which acquires new knowledge from multi-hops QA tasks while conserving old knowledge learned on single-hop QA.
• GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification
  • Zhiyuan Ma, Jianjun Li, Guohui Li, Yongjing Cheng
  • TLDR: We propose a novel Global-to-Local Aggregation and Fission network for fact verification that captures latent logical relations hidden in multiple evidence clues and enables fine-grained and interpretable evidence graph reasoning.
• Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs
  • Zile Qiao, Wei Ye, Tong Zhang, Tong Mo, Weiping Li, Shikun Zhang
  • TLDR: We propose a novel multi-hop multi-task model for multi-word knowledge graph question answering by exploiting the rich semantics of off-the-shelf relation paths.
• Adaptive Threshold Selective Self-Attention for Chinese NER
  • Biao Hu, Zhen Huang, Minghao Hu, Ziwen Zhang, Yong Dou
  • TLDR: We propose a data-driven Adaptive Threshold Selective Self-Attention mechanism that aims to dynamically select the most relevant characters to enhance the Transformer architecture for Chinese named entity recognition.
• Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction
  • Bin Duan, Shusen Wang, Xingxian Liu, Yajing Xu
  • TLDR: We propose a Cluster-aware Pseudo-Labeling method for discovering novel relations by leveraging pseudo-labels for unsupervised open relation extraction.
• Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes
  • Bin Ji, Shasha Li, Shaoduo Gan, Jie Yu, Jun Ma, Huijun Liu, Jing Yang
  • TLDR: We propose EP-Net, an Entity-level Prototypical Network enhanced by dispersedly distributed prototypes.
• Different Data, Different Modalities! Reinforced Data Splitting for Effective Multimodal Information Extraction from Social Media Posts
  • Bo Xu, Shizhou Huang, Ming Du, Hongya Wang, Hui Song, Chaofeng Sha, Yanghua Xiao
  • TLDR: We propose a general data splitting strategy to divide the social media posts into two sets so that these two sets can achieve better performance under the information extraction models of the corresponding modalities.
• Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach
  • Bo Zhou, Chenhao Wang, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, Xiaojian Jiang, Qiuxia Li
  • TLDR: We present a new algorithm for infering possible events related to a specific target.
• Generating Temporally-ordered Event Sequences via Event Optimal Transport
  • Bo Zhou, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, Xiaojian Jiang, Qiuxia Li
  • TLDR: We present a novel model to tackle the generation of temporally-ordered event sequences in texts via Event Optimal Transport (EOT).
• Improving Continual Relation Extraction through Prototypical Contrastive Learning
  • Chengwei Hu, Deqing Yang, Haoliang Jin, Zhen Chen, Yanghua Xiao
  • TLDR: We propose a novel Continual Relation Extraction framework with Contrastive Learning, which is built with a classification network and a prototypical contrastive network to achieve the incremental-class learning of CRE.
• Prompt-based Text Entailment for Low-Resource Named Entity Recognition
  • Dongfang Li, Baotian Hu, Qingcai Chen
  • TLDR: We propose Prompt-based Text Entailment for low-resource named entity recognition, which better leverages knowledge in the PLMs.
• Key Mention Pairs Guided Document-Level Relation Extraction
  • Feng Jiang, Jianwei Niu, Shasha Mo, Shengda Fan
  • TLDR: We propose a novel DocRE model to directly model mention-level relations, containing two modules: a mention- level relation extractor and a key instance classifier.
• A Hybrid Model of Classification and Generation for Spatial Relation Extraction
  • Feng Wang, Peifeng Li, Qiaoming Zhu
  • TLDR: We propose a novel hybrid model HMCGR for spatial relation extraction and show that it outperforms the SOTA baselines significantly.
• Mining Health-related Cause-Effect Statements with High Precision at Large Scale
  • Ferdinand Schlatt, Dieter Bettin, Matthias Hagen, Benno Stein, Martin Potthast
  • TLDR: We propose a new efficient and effective termhood score for predicting the health relatedness of phrases and sentences, which achieves 69% recall at over 90% precision on a web dataset with cause-effect statements.
• Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases
  • Gizem Aydin, Seyed Amin Tabatabaei, George Tsatsaronis, Faegheh Hasibi
  • TLDR: We propose an entity linking model that can perform NIL prediction and overcome data scarcity issues in a time and data-efficient manner.
• KiPT: Knowledge-injected Prompt Tuning for Event Detection
  • Haochen Li, Tong Mo, Hongcheng Fan, Jingkun Wang, Jiaxi Wang, Fuhao Zhang, Weiping Li
  • TLDR: We propose a knowledge-injected prompt tuning strategy for event detection that outperforms strong baselines in few-shot scenarios.
• OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction
  • Hu Cao, Jingye Li, Fangfang Su, Fei Li, Hao Fei, Shengqiong Wu, Bobo Li, Liang Zhao, Donghong Ji
  • TLDR: We present a novel event extraction model for overlapped and nested events, which achieves state-of-the-art results on 3 SOTA benchmarks.
• Joint Language Semantic and Structure Embedding for Knowledge Graph Completion
  • Jianhao Shen, Chenguang Wang, Linyuan Gong, Dawn Song
  • TLDR: We propose a new method for knowledge graph completion that uses probabilistic structured loss to embed the semantics of knowledge graphs in the natural language description of the knowledge triplets.
• Event Detection with Dual Relational Graph Attention Networks
  • Jiaxin Mi, Po Hu, Peng Li
  • TLDR: We propose a novel and effective event detection algorithm that exploits the complementary nature of syntactic and semantic relations to enhance event detection.
• A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck
  • Jie Zhou, Qi Zhang, Qin Chen, Qi Zhang, Liang He, Xuanjing Huang
  • TLDR: We propose a multi-format transfer learning model with variational information bottleneck for event argument extraction.
• RSGT: Relational Structure Guided Temporal Relation Extraction
  • Jie Zhou, Shenpo Dong, Hongkui Tu, Xiaodong Wang, Yong Dou
  • TLDR: We propose a graph neural network based graph neural model for temporal relation extraction and show that it can extract relational structure features that fit for both inter-sentence and intra-sentENCE relations.
• Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations
  • Jinfa Yang, Xianghua Ying, Yongjie Shi, Xin Tong, Ruibin Wang, Taiyan Chen, Bowei Xing
  • TLDR: We propose a new relational embedding model that can encode symmetry/antisymmetry, inversion, composition, multiple relation patterns and learn semantic hierarchies simultaneously.
• Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition
  • Jinzhong Ning, Zhihao Yang, Zhizheng Wang, Yuanyuan Sun, Hongfei Lin, Jian Wang
  • TLDR: We propose a unified bilingual enhancement module for Chinese Named Entity Recognition that captures the interaction of bilinguals and the dependency information within Chinese.
• Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER
  • Jun Zhao, Xin Zhao, WenYu Zhan, Tao Gui, Qi Zhang, Liang Qiao, Zhanzhan Cheng, Shiliang Pu
  • TLDR: We propose a cross-document semantic enhancement method which improves entity recognition from visually-rich documents.
• STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction
  • Junjie Yu, Xing Wang, Jiangjiang Zhao, Chunjie Yang, Wenliang Chen
  • TLDR: We present a simple yet effective self-training approach, named as STAD, for low-resource relation extraction.
• Flat Multi-modal Interaction Transformer for Named Entity Recognition
  • Junyu Lu, Dixiang Zhang, Jiaxing Zhang, Pingjian Zhang
  • TLDR: We propose a novel multi-modal named entity recognition approach based on noun phrases and domain words to obtain visual cues.
• MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification
  • Kailin Zhao, Xiaolong Jin, Saiping Guan, Jiafeng Guo, Xueqi Cheng
  • TLDR: We present a novel meta learning framework for few-shot text classification and propose a task-oriented curriculum learning mechanism to help the meta learner achieve a better generalization ability by learning from different tasks with increasing difficulties.
• A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs
  • Li Cai, Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, Man Lan
  • TLDR: We propose a simple GNN model combined with a temporal information matching mechanism to generate unsupervised alignment seeds between temporal KGs.
• DCT-Centered Temporal Relation Extraction
  • Liang Wang, Peifeng Li, Sheng Xu
  • TLDR: We propose a unified DCT-centered Temporal Relation Extraction model to unify event expressions, timexes and Document Creation Time.
• Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning
  • Lishuang Li, Ruiyuan Lian, Hongbin Lu, Jingyao Tang
  • TLDR: We propose a novel Bio-DocuRE model based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning.
• Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition
  • Matias Rojas, Felipe Bravo-Marquez, Jocelyn Dunstan
  • TLDR: We present a simple, overlooked, yet powerful approach for named entity recognition that achieves state-of-the-art results in the Chilean Waiting List corpus by including pre-trained language models.
• ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification
  • Meiqi Chen, Yixin Cao, Kunquan Deng, Mukai Li, Kun Wang, Jing Shao, Yan Zhang
  • TLDR: We propose a novel event relational graph transformer for document-level event causality identification, which greatly improves the graph construction and improves the accuracy of document-based event classification.
• DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction
  • Mengru Wang, Jianming Zheng, Fei Cai, Taihua Shao, Honghui Chen
  • TLDR: We propose a discriminative rule-based knowledge method for few-shot relation extraction, which alleviates word-overlap and entity-type confusion in meta-learning.
• DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents
  • Mingfei Gao, Le Xue, Chetan Ramaiah, Chen Xing, Ran Xu, Caiming Xiong
  • TLDR: We propose DocQueryNet, a value retrieval method with arbitrary queries for form-like documents to reduce human effort of processing forms.
• DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity Recogition
  • Minghao Tang, Peng Zhang, Yongquan He, Yongxiu Xu, Chengpeng Chao, Hongbo Xu
  • TLDR: We propose a novel machine reading comprehension based framework for cross-domain named entity recognition, which can identify domain-specific semantic differences and mitigate the subtype conflicts between domains.
• Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection
  • Minqian Liu, Shiyu Chang, Lifu Huang
  • TLDR: We propose Episodic Memory Prompts for lifelong event detection.
• Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect
  • Naihao Deng, Yulong Chen, Yue Zhang
  • TLDR: We provide a systematic survey of recent work on text-to-SQL for datasets, methods, and evaluation and provide a detailed analysis of the current state of the art.
• An MRC Framework for Semantic Role Labeling
  • Nan Wang, Jiwei Li, Yuxian Meng, Xiaofei Sun, Han Qiu, Ziyao Wang, Guoyin Wang, Jun He
  • TLDR: We propose to use the machine reading comprehension framework to bridge the semantic connection between the predicate-argument structure and the semantic role structure of sentences.
• PCBERT: Parent and Child BERT for Chinese Few-shot NER
  • Peichao Lai, Feiyang Ye, Lin Zhang, Zhiwei Chen, Yanggeng Fu, Yingjie Wu, Yilei Wang
  • TLDR: We propose a prompt-based Parent and Child BERT for Chinese few-shot NER.
• Label Smoothing for Text Mining
  • Peiyang Liu, Xiangyu Xi, Wei Ye, Shikun Zhang
  • TLDR: We present a novel keyword-based label smoothing method to generate soft labels from hard labels via exploiting the relevance between labels and text instances.
• Diverse Multi-Answer Retrieval with Determinantal Point Processes
  • Poojitha Nandigam, Nikhil Rayaprolu, Manish Shrivastava
  • TLDR: We propose a novel multi-answer retrieval method based on query-passage relevance and passage-passagation correlation to retrieve passages that are both query-relevant and diverse.
• Improving Deep Embedded Clustering via Learning Cluster-level Representations
  • Qing Yin, Zhihua Wang, Yunya Song, Yida Xu, Shuai Niu, Liang Bai, Yike Guo, Xian Yang
  • TLDR: We propose a novel deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations.
• Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts
  • Ran Song, Shizhu He, Suncong Zheng, Shengxiang Gao, Kang Liu, Zhengtao Yu, Jun Zhao
  • TLDR: We propose decoupling mixture-of-graph experts (DMoG) for unseen relations learning, which could represent the unseen relations in the factual graph by fusing ontology and textual graphs, and decouple fusing space and reasoning space to alleviate overfitting for seen relations.
• CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation Extraction
  • Ruri Liu, Shasha Mo, Jianwei Niu, Shengda Fan
  • TLDR: We propose a sentence-level DSRE method that prevents noisy labels from biasing wrongly labeled samples into the wrong classification space on the feature space.
• MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction
  • Saadullah Amin, Pasquale Minervini, David Chang, Pontus Stenetorp, Guenter Neumann
  • TLDR: We present a new biomedical relation extraction benchmark that is based on the SNOMED Clinical Terms knowledge base and a new benchmark for biomedical relation extractions.
• Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective
  • Shihan Dou, Rui Zheng, Ting Wu, SongYang Gao, Junjie Shan, Qi Zhang, Yueming Wu, Xuanjing Huang
  • TLDR: We propose to eliminate spurious correlations in a fine-grained manner from a feature space perspective from a fine graph perspective.
• Event Causality Identification via Derivative Prompt Joint Learning
  • Shirong Shen, Heng Zhou, Tongtong Wu, Guilin Qi
  • TLDR: We propose a new derivative prompt joint learning model for event causality identification, which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem.
• Event Causality Extraction with Event Argument Correlations
  • Shiyao Cui, Jiawei Sheng, Xin Cong, Quangang Li, Tingwen Liu, Jinqiao Shi
  • TLDR: We propose a novel task for event causality extraction from plain text.
• SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER
  • Shuzheng Si, Shuang Zeng, Jiaxing Lin, Baobao Chang
  • TLDR: We propose SCL-RAI to cope with the unlabeled entity problem in Named Entity Recognition datasets.
• A Relation Extraction Dataset for Knowledge Extraction from Web Tables
  • Siffi Singh, Alham Fikri Aji, Gaurav Singh, Christos Christodoulopoulos
  • TLDR: We present REDTab, the largest natural-table relation extraction dataset.
• Automatic Keyphrase Generation by Incorporating Dual Copy Mechanisms in Sequence-to-Sequence Learning
  • Siyu Wang, Jianhui Jiang, Yao Huang, Yin Wang
  • TLDR: We propose a novel model for generating keyphrase generation using sequence labeling and dual copy mechanisms.
• Dependency-aware Prototype Learning for Few-shot Relation Classification
  • Tianshu Yu, Min Yang, Xiaoyan Zhao
  • TLDR: We present a novel dependency-aware prototype learning method for few-shot relation classification.
• MECI: A Multilingual Dataset for Event Causality Identification
  • Viet Dac Lai, Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Thien Huu Nguyen
  • TLDR: We present a new multilingual dataset for event causality identification in multiple non-English languages.
• Method Entity Extraction from Biomedical Texts
  • Waqar Bin Kalim, Robert E. Mercer
  • TLDR: We use linguistic features to find method sentence candidates in biomedical text and train machine learning models to automatically extract method entities from biomedical text.
• Optimal Partial Transport Based Sentence Selection for Long-form Document Matching
  • Weijie Yu, Liang Pang, Jun Xu, Bing Su, Zhenhua Dong, Ji-Rong Wen
  • TLDR: We propose a novel document matching approach that equips existing document matching models with an Optimal Partial Transport (OPT) based component, namely OPT-Match, which selects the sentences that play a major role in matching.
• LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting
  • Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang
  • TLDR: We propose a lightweight tuning paradigm for low-resource NER via pluggable prompting and a pluggability guidance module for learning entity categories.
• Cross-modal Contrastive Attention Model for Medical Report Generation
  • Xiao Song, Xiaodan Zhang, Junzhong Ji, Ying Liu, Pengxu Wei
  • TLDR: We propose a novel Cross-modal Contrastive Attention (CMCA) model to capture both visual and semantic information from similar cases, with mainly two modules: a Visual Contrastive attention Module for refining the unique abnormal regions compared to the retrieved case images; a Cross-Modal Attention Module for matching the positive semantic information.
• Domain-Specific NER via Retrieving Correlated Samples
  • Xin Zhang, Yong Jiang, Xiaobin Wang, Xuming Hu, Yueheng Sun, Pengjun Xie, Meishan Zhang
  • TLDR: We suggest to model correlated samples in the training stage of NER models with correlated entities to help the text understanding.
• Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing
  • Xinyu Zuo, Haijin Liang, Ning Jing, Shuang Zeng, Zhou Fang, Yu Luo
  • TLDR: We propose a type-enriched hierarchical contrastive strategy for FET that can directly model the differences between hierarchical types and improve the ability to distinguish multi-grained similar types.
• Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning
  • Xiusheng Huang, Hang Yang, Yubo Chen, Jun Zhao, Kang Liu, Weijian Sun, Zuyu Zhao
  • TLDR: We propose Pair-Aware and Entity-Enhanced Representation module for document-level relation extraction.
• Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information
  • Xuhui Sui, Ying Zhang, Kehui Song, Baohang Zhou, Guoqing Zhao, Xin Wei, Xiaojie Yuan
  • TLDR: We propose a hierarchical multi-task model to improve the zero-shot entity linking candidate generation task by utilizing the entity typing task as an auxiliary low-level task, which introduces extracted ultra-fine type information into the candidate generation tasks.
• CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction
  • Yequan Wang, Xiang Li, Aixin Sun, Xuying Meng, Huaming Liao, Jiafeng Guo
  • TLDR: Quotation extraction aims to extract quotations from written text.
• Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest
  • Yifan Jin, Jiangmeng Li, Zheng Lian, Chengbo Jiao, Xiaohui Hu
  • TLDR: We propose a method to jointly model semantic and syntactic information from medical texts based on causal explanation theory.
• Aspect-based Sentiment Analysis as Machine Reading Comprehension
  • Yifei Yang, Hai Zhao
  • TLDR: We propose a novel end-to-end framework for aspect-based sentiment analysis, which significantly outperforms existing state-of-the-art models or achieve comparable performance.
• Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing
  • Yifei Yang, Zuchao Li, Hai Zhao
  • TLDR: We propose a holistic structure parsing algorithm for nested NER which can improve NER domain adaptation and also improve corpus-aware statistics for NER.
• DESED: Dialogue-based Explanation for Sentence-level Event Detection
  • Yinyi Wei, Shuaipeng Liu, Jianwei Lv, Xiangyu Xi, Hailei Yan, Wei Ye, Tong Mo, Fan Yang, Guanglu Wan
  • TLDR: Dialogue-based explanation of event detection using information-intensive dialogue.
• Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective
  • Yuanzhou Yao, Zhao Zhang, Yongjun Xu, Chao Li
  • TLDR: We propose to solve the FKGC problem with the data augmentation technique.
• CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction
  • Yubing Ren, Yanan Cao, Fang Fang, Ping Guo, Zheng Lin, Wei Ma, Yi Liu
  • TLDR: We propose a novel event representation enhancement strategy for Document-level Event Extraction.
• COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition
  • Yucheng Huang, Kai He, Yige Wang, Xianli Zhang, Tieliang Gong, Rui Mao, Chen Li
  • TLDR: We propose a novel approach named COntrastive learning with Prompt guiding for few-shot Named Entity Recognition (COPNER) that outperforms state-of-the-art models with a significant margin in most cases.
• Few Clean Instances Help Denoising Distant Supervision
  • Yufang Liu, Ziyin Huang, Yijun Wang, Changzhi Sun, Man Lan, Yuanbin Wu, Xiaofeng Mou, Ding Wang
  • TLDR: We propose a new criterion for clean instance selection based on influence functions and a teacher-student mechanism for controlling purity of intermediate results when bootstrapping the clean set.
• SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
  • Zeng Yang, Linhai Zhang, Deyu Zhou
  • TLDR: We propose a novel multi-task (Seed, Expand and Entail) learning framework for Few-shot NER without using source domain data.
• Ruleformer: Context-aware Rule Mining over Knowledge Graph
  • Zezhong Xu, Peng Ye, Hui Chen, Meng Zhao, Huajun Chen, Wen Zhang
  • TLDR: We propose a transformer-based rule mining approach which takes the context information into consideration in rule mining task and show its effectiveness.
• Are People Located in the Places They Mention in Their Tweets? A Multimodal Approach
  • Zhaomin Xiao, Eduardo Blanco
  • TLDR: We present a new corpus of tweets that contain both text and images to solve the problem of determining whether people are located in the places they mention in their tweets.
• Multi-modal Contrastive Representation Learning for Entity Alignment
  • Zhenxi Lin, Ziheng Zhang, Meng Wang, Yinghui Shi, Xian Wu, Yefeng Zheng
  • TLDR: We propose MCLEA, a Multi-modal Contrastive Learning based Entity Alignment model, to obtain effective joint representations for multi-modAL entity alignment.
• Nonparametric Forest-Structured Neural Topic Modeling
  • Zhihong Zhang, Xuewen Zhang, Yanghui Rao
  • TLDR: We present a nonparametric forest-structured neural topic model by firstly applying the self-attention mechanism to capture parent-child topic relationships, and then build a sparse directed acyclic graph to form a topic forest.
• KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
  • Zhiping Luo, Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu
  • TLDR: We propose a simple yet efficient contrastive learning framework for tensor decomposition based knowledge graph embedding.
• A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection
  • Zhiwei Yang, Jing Ma, Hechang Chen, Hongzhan Lin, Ziyang Luo, Yi Chang
  • TLDR: We propose a novel Coarse-to-fine Cascaded Evidence-Distillation neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones.
• Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning
  • Zhong Qian, Heng Zhang, Peifeng Li, Qiaoming Zhu, Guodong Zhou
  • TLDR: We propose a novel document-level event factuality identification task based on document-based machine reading and transfer learning.
• Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection
  • Zhongqiu Li, Yu Hong, Jie Wang, Shiming He, Jianmin Yao, Guodong Zhou
  • TLDR: We propose a rule-based regulation for translating and retraining neural event detection data in English.
• Finding Influential Instances for Distantly Supervised Relation Extraction
  • Zifeng Wang, Rui Wen, Xi Chen, Shao-Lun Huang, Ningyu Zhang, Yefeng Zheng
  • TLDR: We propose a novel model-agnostic instance sampling method for distant supervision by influence function (IF) and propose a fast influence sampling algorithm that reduces the computational complexity from 2-3x to 1-2x.
• A Simple Model for Distantly Supervised Relation Extraction
  • Ziqin Rao, Fangxiang Feng, Ruifan Li, Xiaojie Wang
  • TLDR: We propose a simple but effective relation extraction algorithm based on bag representations.
• Augmenting Legal Judgment Prediction with Contrastive Case Relations
  • Dugang Liu, Weihao Du, Lei Li, Weike Pan, Zhong Ming
  • TLDR: We propose a new perspective that introduces some contrastive case relations to construct case triples as input, and a corresponding judgment prediction framework with case triple modeling (CTM).
• Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization
  • Elsbeth Turcan, David Wan, Faisal Ladhak, Petra Galuscakova, Sukanta Sen, Svetlana Tchistiakova, Weijia Xu, Marine Carpuat, Kenneth Heafield, Douglas Oard, Kathleen McKeown
  • TLDR: Query-focused summaries of foreign-language, retrieved documents can help a user understand whether a document is actually relevant to the query term.
• Programmable Annotation with Diversed Heuristics and Data Denoising
  • Ernie Chang, Alex Marin, Vera Demberg
  • TLDR: We propose a novel data programming framework that can jointly construct labeled data for language generation and understanding tasks by allowing the annotators to modify an automatically-inferred alignment rule set between sequence labels and text, instead of writing rules from scratch.
• Text-to-Text Extraction and Verbalization of Biomedical Event Graphs
  • Giacomo Frisoni, Gianluca Moro, Lorenzo Balzani
  • TLDR: We present a novel event graph linearization technique and a unified text-to-text approach for biomedical event extraction and verbalization.
• Multimodal Semi-supervised Learning for Disaster Tweet Classification
  • Iustin Sirbu, Tiberiu Sosea, Cornelia Caragea, Doina Caragea, Traian Rebedea
  • TLDR: We propose a semi-supervised learning approach to improve the performance of neural models on several multimodal disaster tweet classification tasks.
• Automated Essay Scoring via Pairwise Contrastive Regression
  • Jiayi Xie, Kaiwei Cai, Li Kong, Junsheng Zhou, Weiguang Qu
  • TLDR: We propose a novel unified Neural Pairwise Contrastive Regression and Pairwise Ranking objective for automated essay scoring.
• Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision
  • Khalil Mrini, Harpreet Singh, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter W. Chang, Emilia Farcas, Ndapa Nakashole
  • TLDR: We present a medical question answering system with knowledge grounding and semantic self-supervision, which can answer long, detailed and informally worded questions submitted by patients.
• A Progressive Framework for Role-Aware Rumor Resolution
  • Lei Chen, Guanying Li, Zhongyu Wei, Yang Yang, Baohua Zhou, Qi Zhang, Xuanjing Huang
  • TLDR: We propose a graph-based model considering the direction and interaction of information flow to implement role-aware rumor resolution.
• Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection
  • Lingwei Wei, Dou Hu, Wei Zhou, Songlin Hu
  • TLDR: We propose a novel dual graph-based model for improving fake news detection.
• A Unified Propagation Forest-based Framework for Fake News Detection
  • Lingwei Wei, Dou Hu, Yantong Lai, Wei Zhou, Songlin Hu
  • TLDR: We propose a novel Unified Propagation Forest-based framework to fully explore latent correlations between propagation trees to improve fake news detection.
• CLoSE: Contrastive Learning of Subframe Embeddings for Political Bias Classification of News Media
  • Michelle YoungJin Kim, Kristen Marie Johnson
  • TLDR: We propose CLoSE, a multi-task BERT-based model which uses contrastive learning to embed indicators of frames from news articles in order to predict political bias.
• Grammatical Error Correction: Are We There Yet?
  • Muhammad Reza Qorib, Hwee Tou Ng
  • TLDR: We show that grammatical error correction systems are not as accurate as we thought, and that there are still classes of errors that they fail to correct.
• CXR Data Annotation and Classification with Pre-trained Language Models
  • Nina Zhou, Ai Ti Aw, Zhuo Han Liu, Cher heng Tan, Yonghan Ting, Wen Xiang Chen, Jordan sim zheng Ting
  • TLDR: We propose a new weak supervision annotation framework for clinical data annotation that is applicable to any given data annotation task.
• uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers
  • Piji Li
  • TLDR: We investigate the data sparsity limitation and overfitting issue of Chinese spelling check.
• Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation
  • Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiaoming Wu
  • TLDR: We propose a plug-and-play pre-trainer for online inference of user and item representations for semantic-enhanced news recommendation.
• Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer
  • Qiong Nan, Danding Wang, Yongchun Zhu, Qiang Sheng, Yuhui Shi, Juan Cao, Jintao Li
  • TLDR: We propose a domain- and instance-level transfer framework for fake news detection, which improves the performance of multi-domain methods.
• Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs
  • Qiongkai Xu, Xuanli He, Lingjuan Lyu, Lizhen Qu, Gholamreza Haffari
  • TLDR: We show that attackers can outperform the original black-box models on transferred domains.
• Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks
  • Rajiv Movva, Jinhao Lei, Shayne Longpre, Ajay Gupta, Chris DuBois
  • TLDR: We show that combining compression methods can synergistically reduce model size, and that practitioners should prioritize (1) quantization, (2) knowledge distillation, and (3) pruning to maximize accuracy vs. model size tradeoffs.
• PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack
  • Rui Zheng, Rong Bao, Qin Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
  • TLDR: We propose a pluggable defense module PlugAT, which improves robustness over several strong baselines on various text classification tasks, whilst training only 9.1% parameters.
• Automatic ICD Coding Exploiting Discourse Structure and Reconciled Code Embeddings
  • Shurui Zhang, Bozheng Zhang, Fuxin Zhang, Bo Sang, Wanchun Yang
  • TLDR: We exploit the discourse structure of clinical notes to improve ICD coding.
• Towards Summarizing Healthcare Questions in Low-Resource Setting
  • Shweta Yadav, Cornelia Caragea
  • TLDR: We present a novel data selection strategy to generate diverse and semantic questions in a low-resource setting with the aim to summarize healthcare questions.
• Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis
  • Siwen Luo, Yihao Ding, Siqu Long, Josiah Poon, Soyeon Caren Han
  • TLDR: We present a novel graph convolutional network for document layout analysis that captures the four main features aspects of document layout components and use it to improve document layout component classification.
• Analytic Automated Essay Scoring Based on Deep Neural Networks Integrating Multidimensional Item Response Theory
  • Takumi Shibata, Masaki Uto
  • TLDR: We propose a new neural model for automated essay scoring that integrates a multidimensional item response theory model and a psychometric model for analytic scoring.
• DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting
  • Timour Igamberdiev, Thomas Arnold, Ivan Habernal
  • TLDR: We present DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable.
• Harnessing Abstractive Summarization for Fact-Checked Claim Detection
  • Varad Bhatnagar, Diptesh Kanojia, Kameswari Chebrolu
  • TLDR: We propose a new workflow for efficiently detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries.
• Learning to Generate Explanation from e-Hospital Services for Medical Suggestion
  • Wei-Lin Chen, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen
  • TLDR: We propose a novel discourse-aware mechanism for explanation generation of neural models with narrative and causal structure for health consulting.
• DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis
  • Xian Wu, Shuxin Yang, Zhaopeng Qiu, Shen Ge, Yangtian Yan, Xingwang Wu, Yefeng Zheng, S. Kevin Zhou, Li Xiao
  • TLDR: We propose DeltaNet to generate medical reports automatically based on a conditional report.
• MCS: An In-battle Commentary System for MOBA Games
  • Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li
  • TLDR: Generative system for in-battle real-time commentary in mobile MOBA games.
• A Two Stage Adaptation Framework for Frame Detection via Prompt Learning
  • Xinyi Mou, Zhongyu Wei, Changjian Jiang, Jiajie Peng
  • TLDR: We propose a two-stage adaptation framework for frame detection and adaptation.
• Summarizing Patients’ Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models
  • Yanjun Gao, Dmitriy Dligach, Timothy Miller, Dongfang Xu, Matthew M. M. Churpek, Majid Afshar
  • TLDR: We propose a new NLP task that aims to generate a list of problems in a patient’s daily care plan using input from the provider’S progress notes during hospitalization.
• Human-in-the-loop Robotic Grasping Using BERT Scene Representation
  • Yaoxian Song, Penglei Sun, Pengfei Fang, Linyi Yang, Yanghua Xiao, Yue Zhang
  • TLDR: We propose a human-in-the-loop framework for robotic grasping in cluttered scenes, investigating a language interface to the grasping process, which allows the user to intervene by natural language commands.
• Automated Chinese Essay Scoring from Multiple Traits
  • Yaqiong He, Feng Jiang, Xiaomin Chu, Peifeng Li
  • TLDR: We propose a hierarchical multi-task trait scorer HMTS to evaluate the quality of essays from multiple traits.
• Semantic-Preserving Adversarial Code Comprehension
  • Yiyang Li, Hongqiu Wu, Hai Zhao
  • TLDR: We propose Semantic-Preserving Adversarial Code Embeddings to find the worst-case adversarial attacks while forcing the model to predict the correct labels under these worst cases.
• Continually Detection, Rapidly React: Unseen Rumors Detection Based on Continual Prompt-Tuning
  • Yuhui Zuo, Wei Zhu, Guoyong GUET Cai
  • TLDR: We propose a Continual Prompt-Tuning RD framework that avoids catastrophic forgetting (CF) of upstream tasks during sequential task learning and enables bidirectional knowledge transfer between domain tasks.
• AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications
  • Yusen Zhang, Zhongli Li, Qingyu Zhou, Ziyi Liu, Chao Li, Mina Ma, Yunbo Cao, Hongzhi Liu
  • TLDR: We propose a multimodal approach for handwritten cloze tests correction and show that it outperforms OCR-based methods by a large margin.
• TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding
  • Zichen Liu, Xuyuan Liu, Yanlong Wen, Guoqing Zhao, Fen Xia, Xiaojie Yuan
  • TLDR: We propose a Tree-enhanced Multimodal Attention Network for ICD coding based on structured multimodal medical data.
• Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing
  • Ziming Huang, Zhuoxuan Jiang, Ke Wang, Juntao Li, Shanshan Feng, Xian-Ling Mao
  • TLDR: We propose a novel multi-task learning framework for human-bot symbiosis dialog routing and show that it can improve the state-of-the-art performance by 8.7%/11.8% on RMSE metric and 2.2%/4.4% on F1 metric.
• Negation, Coordination, and Quantifiers in Contextualized Language Models
  • Aikaterini-Lida Kalouli, Rita Sevastjanova, Christin Beck, Maribel Romero
  • TLDR: We explore whether the semantic constraints of function words are learned and how the surrounding context impacts their embeddings.
• Tales and Tropes: Gender Roles from Word Embeddings in a Century of Children’s Books
  • Anjali Adukia, Patricia Chiril, Callista Christ, Anjali Das, Alex Eble, Emileigh Harrison, Hakizumwami Birali Runesha
  • TLDR: We measure the gendered depiction of central domains of social life in 100 years of highly influential children’s books.
• CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations
  • Borun Chen, Hongyin Tang, Jiahao Bu, Kai Zhang, Jingang Wang, Qifan Wang, Hai-Tao Zheng, Wei Wu, Liqian Yu
  • TLDR: We propose a simple yet effective PLM CLOWER, which encodes the coarse-grained information (i.e., words) into the fine-grain representations (i) characters and (b) charactERs.
• On the Nature of BERT: Correlating Fine-Tuning and Linguistic Competence
  • Federica Merendi, Felice Dell’Orletta, Giulia Venturi
  • TLDR: We show to what extent a wide range of linguistic phenomena are forgotten across 50 epochs of fine-tuning, and how the preserved linguistic knowledge is correlated with the resolution of the fine-tuning task.
• LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency
  • Haoxiang Shi, Rongsheng Zhang, Jiaan Wang, Cen Wang, Yinhe Zheng, Tetsuya Sakai
  • TLDR: We propose LayerConnect (hypernetwork-assisted inter-layer connectors) to enhance inference efficiency of pre-trained language models.
• Effect of Post-processing on Contextualized Word Representations
  • Hassan Sajjad, Firoj Alam, Fahim Dalvi, Nadir Durrani
  • TLDR: We show that post-processing of contextualized embeddings obtained from different layers of pre-trained language models improves the performance of lexical and sequence classification tasks.
• Does BERT Rediscover a Classical NLP Pipeline?
  • Jingcheng Niu, Wenjie Lu, Gerald Penn
  • TLDR: We show that BERT is linguistically founded, but not structured in a pipeline-like way.
• HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph
  • Qitong Wang, Mohammed J Zaki
  • TLDR: We propose a new model, HG2Vec, that learns word embeddings utilizing only dictionaries and thesauri.
• Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing
  • Ran Ji, Jianmin Ji
  • TLDR: We present a structure-aware self-attention language model to capture structural information of target representations and propose a knowledge distillation based approach to incorporating the target language model into a seq2seq model, where grammar rules or sketches are not required in the training process.
• Enhancing Contextual Word Representations Using Embedding of Neighboring Entities in Knowledge Graphs
  • Ryoko Tokuhisa, Keisuke Kawano, Akihiro Nakamura, Satoshi Koide
  • TLDR: We propose a new method for integrating knowledge graphs into pre-trained language models.
• Generic Overgeneralization in Pre-trained Language Models
  • Sello Ralethe, Jan Buys
  • TLDR: We show that pre-trained language models suffer from overgeneralization and tend to treat quantified generic statements such as “all ducks lay eggs” as if they were true generics.
• How about Time? Probing a Multilingual Language Model for Temporal Relations
  • Tommaso Caselli, Irene Dini, Felice Dell’Orletta
  • TLDR: We present a comprehensive set of probing experiments using a multilingual language model, XLM-R, for temporal relation classification between events in four languages.
• CogBERT: Cognition-Guided Pre-trained Language Models
  • Xiao Ding, Bowen Chen, Li Du, Bing Qin, Ting Liu
  • TLDR: We propose CogBERT, a framework that can induce fine-grained cognitive features from cognitive data and incorporate cognitive features into BERT by adaptively adjusting the weight of cognitive features for different NLP tasks.
• Can Transformers Process Recursive Nested Constructions, Like Humans?
  • Yair Lakretz, Théo Desbordes, Dieuwke Hupkes, Stanislas Dehaene
  • TLDR: We study if state-of-the-art Transformer LMs do any better on embedded dependencies within nested constructions.
• NSP-BERT: A Prompt-based Few-Shot Learner through an Original Pre-training Task —— Next Sentence Prediction
  • Yi Sun, Yu Zheng, Chao Hao, Hangping Qiu
  • TLDR: We present a sentence-level prompt-based method for few-shot NLP tasks that is competitive compared to PET and EFL.
• MetaPrompting: Learning to Learn Better Prompts
  • Yutai Hou, Hongyuan Dong, Xinghao Wang, Bohan Li, Wanxiang Che
  • TLDR: MetaPrompting is a generalized soft prompting method that automatically finds better prompt initialization for soft prompts.
• Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models
  • Ze-Feng Gao, Peiyu Liu, Wayne Xin Zhao, Zhong-Yi Lu, Ji-Rong Wen
  • TLDR: We propose a parameter-efficient Mixture-of-Experts architecture by sharing information across experts.
• Pre-trained Token-replaced Detection Model as Few-shot Learner
  • Zicheng Li, Shoushan Li, Guodong Zhou
  • TLDR: We propose a novel approach to few-shot learning with pre-trained token-replaced detection models like ELECTRA.
• Evaluating Diversity of Multiword Expressions in Annotated Text
  • Adam Lion-Bouton, Yagmur Ozturk, Agata Savary, Jean-Yves Antoine
  • TLDR: We propose a new measure of diversity for multiword expression annotation produced by automatic annotation systems and evaluate its pertinence for multi-word expressions in annotated texts.
• CausalQA: A Benchmark for Causal Question Answering
  • Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Blübaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast
  • TLDR: We present a new benchmark corpus of causal questions with answers and propose a novel typology for answering them.
• MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction
  • Amir Pouran Ben Veyseh, Nicole Meister, Seunghyun Yoon, Rajiv Jain, Franck Dernoncourt, Thien Huu Nguyen
  • TLDR: We propose a new dataset for multilingual and multi-domain acronym extraction.
• Curating a Large-Scale Motivational Interviewing Dataset Using Peer Support Forums
  • Anuradha Welivita, Pearl Pu
  • TLDR: We use the data from Reddit and other peer support platforms to identify the differences between responses from counselors and peers and analyze their relationship to therapeutic chatbots.
• CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers
  • Baoxin Wang, Xingyi Duan, Dayong Wu, Wanxiang Che, Zhigang Chen, Guoping Hu
  • TLDR: We propose a cross-sentence Chinese text correction dataset for native speakers.
• RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips
  • Bo Xu, Hongtong Zhang, Jian Wang, Xiaokun Zhang, Dezhi Hao, Linlin Zong, Hongfei Lin, Fenglong Ma
  • TLDR: We construct a Chinese medical dialogue dataset based on real medical consultation.
• TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media
  • Daniel Loureiro, Aminette D’Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa-Anke, Leonardo Neves, Francesco Barbieri, Jose Camacho-Collados
  • TLDR: We present TempoWiC, a new benchmark for social media-based meaning shift, which is a challenging benchmark for language models specialized in social media.
• Automatic Generation of Large-scale Multi-turn Dialogues from Reddit
  • Daniil Huryn, William M. Hutsell, Jinho D. Choi
  • TLDR: We present novel methods to automatically convert posts and their comments from discussion forums such as Reddit into multi-turn dialogues.
• ConFiguRe: Exploring Discourse-level Chinese Figures of Speech
  • Dawei Zhu, Qiusi Zhan, Zhejian Zhou, Yifan Song, Jiebin Zhang, Sujian Li
  • TLDR: We propose a novel framework for contextualized figure recognition in Chinese, which aims at extracting a figurative unit from discourse-level context, classifying the figurative units into the right figure type, and classifying them into the appropriate figure types.
• Twitter Topic Classification
  • Dimosthenis Antypas, Asahi Ushio, Jose Camacho-Collados, Vitor Silva, Leonardo Neves, Francesco Barbieri
  • TLDR: We present a new task based on tweet topic classification and release two associated datasets.
• Layer or Representation Space: What Makes BERT-based Evaluation Metrics Robust?
  • Doan Nam Long Vu, Nafise Sadat Moosavi, Steffen Eger
  • TLDR: We show that the robustness of embedding-based metrics for text generation is not as strong as the correlation with human evaluations on standard benchmarks.
• Evaluating the Performance of Transformer-based Language Models for Neuroatypical Language
  • Duanchen Liu, Zoey Liu, Qingyun Yang, Yujing Huang, Emily Prud’hommeaux
  • TLDR: We present a transformer-based framework for identifying linguistic features associated with social aspects of communication using a corpus of conversations between adults with and without ASD and neurotypical conversational partners produced while engaging in collaborative tasks.
• TERMinator: A System for Scientific Texts Processing
  • Elena Bruches, Olga Tikhobaeva, Yana Dementyeva, Tatiana Batura
  • TLDR: We present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition and comparison of different approaches for relation extraction.
• LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization
  • Fajri Koto, Timothy Baldwin, Jey Han Lau
  • TLDR: We present a novel approach to document summarization that combines human-written abstractive summaries, absent keyphrases, and titles.
• Understanding Attention for Vision-and-Language Tasks
  • Feiqi Cao, Soyeon Caren Han, Siqu Long, Changwei Xu, Josiah Poon
  • TLDR: We analyze the role of attention mechanism in Vision-and-Language tasks and provide useful insights of the importance of each attention alignment score calculation when applied at the training phase of VL tasks, commonly ignored in attention-based cross modal models, and/or pretrained models.
• Effective Data Augmentation for Sentence Classification Using One VAE per Class
  • Frédéric Piedboeuf, Philippe Langlais
  • TLDR: We present a new method for generating textual data augmentation that outperforms other generative data augmentations.
• NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation
  • Giacomo Frisoni, Antonella Carbonaro, Gianluca Moro, Andrea Zammarchi, Marco Avagnano
  • TLDR: We present NLG-Metricverse, an end-to-end open-source library for NLG evaluation based on Python.
• TestAug: A Framework for Augmenting Capability-based NLP Tests
  • Guanqun Yang, Mirazul Haque, Qiaochu Song, Wei Yang, Xueqing Liu
  • TLDR: We propose a new approach to capability-based NLP testing that uses GPT-3 engine to generate test suites and validate the correctness of the generated suites.
• KoCHET: A Korean Cultural Heritage Corpus for Entity-related Tasks
  • Gyeongmin Kim, Jinsung Kim, Junyoung Son, Heuiseok Lim
  • TLDR: We propose KoCHET - a cultural heritage corpus for the typical entity-related tasks, i.e., named entity recognition (NER), relation extraction (RE), and entity typing (ET).
• MonoByte: A Pool of Monolingual Byte-level Language Models
  • Hugo Abonizio, Leandro Rodrigues de Souza, Roberto Lotufo, Rodrigo Nogueira
  • TLDR: We present 10 monolingual byte-level models rigorously pretrained on multilingual and even monolingually corpora that achieve competitive performance to multilingual models.
• Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings
  • Jason Ingyu Choi, Saar Kuzi, Nikhita Vedula, Jie Zhao, Giuseppe Castellucci, Marcus Collins, Shervin Malmasi, Oleg Rokhlenko, Eugene Agichtein
  • TLDR: We present Wizard of Tasks, a corpus of conversations in two domains for conversational task assistants.
• K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment
  • Jean Lee, Taejun Lim, Heejun Lee, Bogeun Jo, Yangsok Kim, Heegeun Yoon, Soyeon Caren Han
  • TLDR: We present a new multi-label dataset for hate speech detection in Korean language patterns and show strong results.
• Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model
  • Jeska Buhmann, Maxime De Bruyn, Ehsan Lotfi, Walter Daelemans
  • TLDR: We present a large FAQ corpus with large groups of semantically equivalent human-paraphrased questions and show that large groups are important for obtaining well-performing intent classification models.
• Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation
  • Junyu Luo, Junxian Lin, Chi Lin, Cao Xiao, Xinning Gui, Fenglong Ma
  • TLDR: We propose a new dataset for clinical language simplification and a new model for it.
• WikiHan: A New Comparative Dataset for Chinese Languages
  • Kalvin Chang, Chenxuan Cui, Youngmin Kim, David R. Mortensen
  • TLDR: We present a dataset of Chinese varieties and Middle Chinese varieties that contains structured form and structured word data for comparative linguistics and Chinese NLP.
• Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows
  • Keisuke Shirai, Atsushi Hashimoto, Taichi Nishimura, Hirotaka Kameko, Shuhei Kurita, Yoshitaka Ushiku, Shinsuke Mori
  • TLDR: We present a new multimodal dataset called Visual Recipe Flow, which enables us to learn a cooking action result for each object in a recipe text.
• IMPARA: Impact-Based Metric for GEC Using Parallel Data
  • Koki Maeda, Masahiro Kaneko, Naoaki Okazaki
  • TLDR: We propose an Impact-based Metric for GECC for grammatical error correction using parallel data.
• Evons: A Dataset for Fake and Real News Virality Analysis and Prediction
  • Kriste Krstovski, Angela Soomin Ryu, Bruce Kogut
  • TLDR: We present a novel collection of news articles originating from fake and real news media sources for the analysis and prediction of news virality.
• Are Pretrained Multilingual Models Equally Fair across Languages?
  • Laura Cabello Piqueras, Anders Søgaard
  • TLDR: We investigate the group fairness of multilingual language models and show that across the four target languages, the three models exhibit different levels of group disparity, e.g., exhibiting near-equal risk for Spanish, but high levels of disparity for German.
• Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios
  • Mana Ashida, Saku Sugawara
  • TLDR: We present a novel dataset of stories with multiple possible endings and show that even strong pretrained language models struggle to answer the questions consistently.
• DiaBiz.Kom - towards a Polish Dialogue Act Corpus Based on ISO 24617-2 Standard
  • Marcin Oleksy, Jan Wieczorek, Dorota Drużyłowska, Julia Klyus, Aleksandra Domogała, Krzysztof Hwaszcz, Hanna Kędzierska, Daria Mikoś, Anita Wróż
  • TLDR: This article presents the specification and evaluation of DiaBiz.Kom – the corpus of dialogue texts in Polish.
• Towards Explainable Evaluation of Language Models on the Semantic Similarity of Visual Concepts
  • Maria Lymperaiou, George Manoliadis, Orfeas Menis Mastromichalakis, Edmund G. Dervakos, Giorgos Stamou
  • TLDR: We propose explainable evaluation metrics for high-performing pre-trained language models, which provide insights into the inabilities of widely used metrics.
• Establishing Annotation Quality in Multi-label Annotations
  • Marian Marchal, Merel Scholman, Frances Yung, Vera Demberg
  • TLDR: We present a bootstrapped adjusted agreement coefficient for multi-label annotations and show how it can improve the accuracy of multi-coder agreement.
• Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments
  • Michael Wiegand, Rebecca Wilm, Katja Markert
  • TLDR: We present a new dataset comprising tweets for the novel task of detecting biographically relevant utterances.
• BECEL: Benchmark for Consistency Evaluation of Language Models
  • Myeongjun Jang, Deuk Sin Kwon, Thomas Lukasiewicz
  • TLDR: We propose a new benchmark for language models that allows us to evaluate a model on 19 test cases, distinguished by multiple types of consistency and diverse downstream tasks.
• KoBEST: Korean Balanced Evaluation of Significant Tasks
  • Myeongjun Jang, Dohyung Kim, Deuk Sin Kwon, Eric Davis
  • TLDR: We propose a new benchmark for Korean language downstream evaluation of significant tasks, which consists of five Korean-language downstream tasks.
• A New Public Corpus for Clinical Section Identification: MedSecId
  • Paul Landes, Kunal Patel, Sean S. Huang, Adam Webb, Barbara Di Eugenio, Cornelia Caragea
  • TLDR: We present a method for segmenting the sections of clinical medical domain documentation and show a relationship between medical concepts across sections using principal component analysis.
• A Data-driven Approach to Named Entity Recognition for Early Modern French
  • Pedro Ortiz Suarez, Simon Gabay
  • TLDR: We develop a new corpus for named entity recognition for historical French and fine-tuned existing architectures for Early Modern French.
• Reproducibility and Automation of the Appraisal Taxonomy
  • Pradeesh Parameswaran, Andrew Trotman, Veronica Liesaputra, David Eyers
  • TLDR: Automating Appraisal classification is not practical for coarse-level categories of the taxonomy.
• Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline
  • Ruixue Liu, Shaozu Yuan, Aijun Dai, Lei Shen, Tiangang Zhu, Meng Chen, Xiaodong He
  • TLDR: We present a benchmark dataset for few-shot table understanding and a novel table PLM for Chinese table pre-training.
• Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical Arabic Literature
  • Sajawel Ahmed, Rob van der Goot, Misbahur Rehman, Carl Kruse, Ömer Özsoy, Alexander Mehler, Gemma Roig
  • TLDR: We present a novel multi-task multi-tasking task for Classical Arabic literature and show that it is challenging to classify and analyze Classical Arabic Literature.
• Resource of Wikipedias in 31 Languages Categorized into Fine-Grained Named Entities
  • Satoshi Sekine, Kouta Nakayama, Masako Nomoto, Maya Ando, Asuka Sumida, Koji Matsuda
  • TLDR: We present a resource of Wikipedias in 31 languages categorized into Extended Named Entity (ENE), which has 219 fine-grained NE categories.
• Accuracy meets Diversity in a News Recommender System
  • Shaina Raza, Syed Raza Bashir, Usman Naseem
  • TLDR: We propose a deep neural network based on a two-tower architecture that learns news representation through a news item tower and users’ representations through a query tower.
• Dynamic Nonlinear Mixup with Distance-based Sample Selection
  • Shaokang Zhang, Lei Jiang, Jianlong Tan
  • TLDR: We propose a novel nonlinear mixup with distance-based sample selection and use it to fuse sample pairs.
• MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition
  • Shervin Malmasi, Anjie Fang, Besnik Fetahu, Sudipta Kar, Oleg Rokhlenko
  • TLDR: We present AnonData, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets.
• Extracting a Knowledge Base of COVID-19 Events from Social Media
  • Shi Zong, Ashutosh Baheti, Wei Xu, Alan Ritter
  • TLDR: We present a manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, denied treatment, claimed cures and preventions.
• Accounting for Language Effect in the Evaluation of Cross-lingual AMR Parsers
  • Shira Wein, Nathan Schneider
  • TLDR: We present three multilingual adaptations of monolingual AMR evaluation metrics and compare the performance of these metrics to sentence-level human judgments.
• QSTS: A Question-Sensitive Text Similarity Measure for Question Generation
  • Sujatha Das Gollapalli, See-Kiong Ng
  • TLDR: We propose Question-Sensitive Text Similarity measure for comparing two questions by characterizing their target intent based on question class, named-entity, and semantic similarity information from the two questions.
• Noun-MWP: Math Word Problems Meet Noun Answers
  • Taehun Cha, Jaeheun Jung, Donghun Lee
  • TLDR: We introduce a new type of problems for math word problem (MWP) solvers, named Noun-MWPs, whose answer is a non-numerical string containing a noun from the problem text.
• ViNLI: A Vietnamese Corpus for Studies on Open-Domain Natural Language Inference
  • Tin Van Huynh, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
  • TLDR: We introduce ViNLINLI, an open-domain and high-quality corpus for evaluating Vietnamese NLI models, which is created and evaluated with a strict process of quality control.
• InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples
  • Venelin Kovatchev, Mariona Taulé
  • TLDR: We present InferES - an original corpus for Natural Language Inference in European Spanish.
• ParaZh-22M: A Large-Scale Chinese Parabank via Machine Translation
  • Wenjie Hao, Hongfei Xu, Deyi Xiong, Hongying Zan, Lingling Mu
  • TLDR: We propose to construct a large Chinese parabank of sentence-level paraphrases based on one-to-many sentence translation data and machine translation.
• ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
  • Xing Wu, Chaochen Gao, Liangjun Zang, Jizhong Han, Zhongyuan Wang, Songlin Hu
  • TLDR: We propose a new sentence embedding method that is more efficient than the current state-of-the-art unsupervised method, and it outperforms the state- of-the -art unsup-SimCSE on several benchmark datasets.
• Measuring Robustness for NLP
  • Yu Yu, Abdul Rafae Khan, Jia Xu
  • TLDR: We propose a new robustness measure for NLP models that is more comparable to human evaluation and improves the final prediction accuracy over various domains.
• CSL: A Large-scale Chinese Scientific Literature Dataset
  • Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao, Hui Zhang
  • TLDR: We present a large-scale Chinese Scientific Literature dataset, which contains the titles, abstracts, keywords and academic fields of 396k papers.
• Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization
  • Zhengyuan Liu, Shikang Ni, Ai Ti Aw, Nancy F. Chen
  • TLDR: We propose a new computational approach for creole translation and text normalization of Singlish messages, which can improve the performance of downstream tasks like stance detection.
• CINO: A Chinese Minority Pre-trained Language Model
  • Ziqing Yang, Zihang Xu, Yiming Cui, Baoxin Wang, Min Lin, Dayong Wu, Zhigang Chen
  • TLDR: We propose CINO (Chinese Minority Pre-trained Language Model), a multilingual pre-trained language model for Chinese minority languages.
• One Word, Two Sides: Traces of Stance in Contextualized Word Representations
  • Aina Garí Soler, Matthieu Labeau, Chloé Clavel
  • TLDR: We investigate whether the way we use words is influenced by our opinion.
• Prepositions Matter in Quantifier Scope Disambiguation
  • Aleksander Leczkowski, Justyna Grudzińska, Manuel Vargas Guzmán, Aleksander Wawer, Aleksandra Siemieniuk
  • TLDR: We present a new approach to quantifier scope disambiguation by incorporating into a machine learning model our knowledge about relations, as conveyed by a manageable closed class of function words: prepositions.
• Modelling Commonsense Properties Using Pre-Trained Bi-Encoders
  • Amit Gajbhiye, Luis Espinosa-Anke, Steven Schockaert
  • TLDR: We show that language models can be fine-tuned to capture commonsense properties of everyday concepts and properties.
• COIN – an Inexpensive and Strong Baseline for Predicting Out of Vocabulary Word Embeddings
  • Andrew Schneider, Lihong He, Zhijia Chen, Arjun Mukherjee, Eduard Dragut
  • TLDR: We propose a fast method for predicting word embeddings for out of vocabulary terms that makes use of the surrounding terms of the unknown term and the hidden context layer of the word2vec model.
• DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing
  • Bin Li, Miao Gao, Yunlong Fan, Yikemaiti Sataer, Zhiqiang Gao, Yaocheng Gui
  • TLDR: We propose a dynamic graph learning framework and apply it to semantic dependency parsing, for jointly learning graph structure and graph representations.
• Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion
  • Chen Chen, Yufei Wang, Bing Li, Kwok-Yan Lam
  • TLDR: We propose KG-S2S, a Seq2Seq generative framework that could tackle different verbalizable graph structures by unifying the representation of KG facts into “flat” text, regardless of their original form.
• Modelling Frequency, Attestation, and Corpus-Based Information with OntoLex-FrAC
  • Christian Chiarcos, Elena-Simona Apostol, Besim Kabashi, Ciprian-Octavian Truică
  • TLDR: We present a new module for Frequency, Attestations, Corpus-Based Information, and Corpus-based Information that is intended to complement OntoLex-Lemon with the necessary vocabulary to represent major types of information found in or automatically derived from corpora, for applications in both language technology and the language sciences.
• Contrast Sets for Stativity of English Verbs in Context
  • Daniel Chen, Alexis Palmer
  • TLDR: We show that the classifier of verbs in context is more complex than we thought, and that it is possible to generalize beyond particular test sets.
• Multilingual and Multimodal Topic Modelling with Pretrained Embeddings
  • Elaine Zosa, Lidia Pivovarova
  • TLDR: We present a novel multimodal multilingual topic model for comparable data that maps texts from multiple languages and images into a shared topic space.
• Zero-shot Script Parsing
  • Fangzhou Zhai, Vera Demberg, Alexander Koller
  • TLDR: We propose a zero-shot learning approach to script knowledge that is applicable to a variety of NLP tasks.
• Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension
  • Guobiao Zhang, Wenpeng Lu, Xueping Peng, Shoujin Wang, Baoshuo Kan, Rui Yu
  • TLDR: We propose a novel WSD method with knowledge-enhanced and local self-attention-based extractive sense comprehension.
• A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit
  • Jivnesh Sandhan, Ashish Gupta, Hrishikesh Terdalkar, Tushar Sandhan, Suvendu Samanta, Laxmidhar Behera, Pawan Goyal
  • TLDR: We propose a novel multi-task learning architecture for Sanskrit Compound Type Identification task which incorporates contextual information and enriches the complementary syntactic information using morphological tagging and dependency parsing as two auxiliary tasks.
• Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment
  • Lorenzo Bertolini, Julie Weeds, David Weir
  • TLDR: We propose PLANE, a new benchmark to test models on fine-grained compositional entailment using adjective-noun phrases.
• Does BERT Recognize an Agent? Modeling Dowty’s Proto-Roles with Contextual Embeddings
  • Mattia Proietti, Gianluca Lebani, Alessandro Lenci
  • TLDR: We investigate the semantic properties of the verb embeddings of contextual embeddents and show that they are able to infer the semantic relations that, according to Dowty’s Proto-Roles theory, a verbal argument receives by virtue of its role in the event described by the verb.
• Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders
  • Qiwei Peng, David Weir, Julie Weeds
  • TLDR: We propose to combine sentence encoders with an alignment component for meaning comparison tasks and show improved performance and interpretability.
• CILex: An Investigation of Context Information for Lexical Substitution Methods
  • Sandaru Seneviratne, Elena Daskalaki, Artem Lenskiy, Hanna Suominen
  • TLDR: We proposed CILex, a novel approach for lexical substitution based on contextual sentence embeddings and contextual word embeddements.
• Emotion Enriched Retrofitted Word Embeddings
  • Sapan Shah, Sreedhar Reddy, Pushpak Bhattacharyya
  • TLDR: We present a novel retrofitting method for updating the vectors of emotion bearing words like fun, offence, angry, etc. with emotion-bearing embeddings.
• Metaphor Detection via Linguistics Enhanced Siamese Network
  • Shenglong Zhang, Ying Liu
  • TLDR: We present MisNet, a novel model for word level metaphor detection.
• Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing
  • Shilin Zhou, Qingrong Xia, Zhenghua Li, Yu Zhang, Yu Hong, Min Zhang
  • TLDR: We propose a word-based graph parsing task based on span-based span-to-span SRL and show that it is significantly better than previous word-by-word graph parsing tasks.
• Unsupervised Lexical Substitution with Decontextualised Embeddings
  • Takashi Wada, Timothy Baldwin, Yuji Matsumoto, Jey Han Lau
  • TLDR: We propose a new unsupervised method for lexical substitution using pre-trained language models.
• Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations
  • Wessel Poelman, Rik van Noord, Johan Bos
  • TLDR: We present a rule-based Universal Dependency Parser that maps a syntactic dependency tree to a formal meaning representation based on Discourse Representation Theory.
• Multilingual Word Sense Disambiguation with Unified Sense Representation
  • Ying Su, Hongming Zhang, Yangqiu Song, Tong Zhang
  • TLDR: We propose to build knowledge and supervised based Multilingual Word Sense Disambiguation systems by transferring annotations from rich sourced languages.
• A Transition-based Method for Complex Question Understanding
  • Yu Xia, Wenbin Jiang, Yajuan Lyu, Sujian Li
  • TLDR: We propose a transition-based method for parsing complex question understanding using question decomposition meaning representation.
• Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments
  • Yu Zhang, Qingrong Xia, Shilin Zhou, Yong Jiang, Guohong Fu, Min Zhang
  • TLDR: We propose to reduce semantic role labeling to a tree parsing task by considering flat argument spans as latent subtrees.
• Noisy Label Regularisation for Textual Regression
  • Yuxia Wang, Timothy Baldwin, Karin Verspoor
  • TLDR: We propose a simple noisy label detection method that prevents error propagation from the input layer and improve generalisation performance.
• Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language
  • Amir Bialer, Daniel Izmaylov, Avi Segal, Oren Tsur, Yossi Levi-Belz, Kobi Gal
  • TLDR: We propose a new language model for automatic suicide detection in low-resource languages, which outperforms a wide range of strong baselines.
• Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?
  • Aniruddha Roy, Rupak Kumar Thakur, Isha Sharma, Ashim Gupta, Amrith Krishna, Sudeshna Sarkar, Pawan Goyal
  • TLDR: We propose a meta-learning approach to few-shot question generation in few-shots and show that it improves the performance of the base model in few shot settings and even works better than some heavily parameterized models.
• Revisiting Syllables in Language Modelling and Their Application on Low-Resource Machine Translation
  • Arturo Oncevay, Kervy Dante Rivas Rojas, Liz Karen Chavez Sanchez, Roberto Zariquiey
  • TLDR: Syllables are a good input for language modelling and machine translation, and they can be used for both pairwise and multilingual systems.
• Aligning Multilingual Embeddings for Improved Code-switched Natural Language Understanding
  • Barah Fazili, Preethi Jyothi
  • TLDR: We present a novel idea of training multilingual models with alignment objectives using parallel text so as to explicitly align word representations with the same underlying semantics across languages.
• Fashioning Local Designs from Generic Speech Technologies in an Australian Aboriginal Community
  • Éric Le Ferrand, Steven Bird, Laurent Besacier
  • TLDR: We explore the potential of speech recognition in an Aboriginal community and show how to use it to improve comprehension and engagement.
• Few-Shot Pidgin Text Adaptation via Contrastive Fine-Tuning
  • Ernie Chang, Jesujoba O. Alabi, David Ifeoluwa Adelani, Vera Demberg
  • TLDR: We propose to generate utterances in Pidgin-English conversation corpus by leveraging the proximity of the source and target languages, and utilizing positive and negative examples in constrastive training objectives.
• Penalizing Divergence: Multi-Parallel Translation for Low-Resource Languages of North America
  • Garrett Nicolai, Changbing Yang, Miikka Silfverberg
  • TLDR: We propose a regularizer for multi-parallel translation, which penalizes the translation model when it represents source sentences with identical target translations in divergent ways.
• Assessing Digital Language Support on a Global Scale
  • Gary F. Simons, Abbey L. L. Thomas, Chad K. K. White
  • TLDR: We develop a method for quantifying and monitoring digital language support and monitoring it on a global scale.
• Persian Natural Language Inference: A Meta-learning Approach
  • Heydar Soudani, Mohammad Hassan Mojab, Hamid Beigy
  • TLDR: Meta-learning for natural language inference in Persian.
• Global Readiness of Language Technology for Healthcare: What Would It Take to Combat the Next Pandemic?
  • Ishani Mondal, Kabir Ahuja, Mohit Jain, Jacki O’Neill, Kalika Bali, Monojit Choudhury
  • TLDR: We explore the state of language technology for healthcare across the world’s languages and identify the gaps in the current state of the art.
• Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning
  • Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, Dietrich Klakow
  • TLDR: We perform multilingual adaptive fine-tuning on 17 most-resourced African languages and three other high-resource languages widely spoken on the African continent to encourage cross-lingual transfer learning.
• Noun Class Disambiguation in Runyankore and Related Languages
  • Joan Byamugisha
  • TLDR: We developed a new method for computing Bantu language text that uses semantic generalizations of the nearest neighbors of query words as semantic generalization to identify singular nouns with the same class prefix but belonging to different noun classes.
• Improving Low-resource RRG Parsing with Cross-lingual Self-training
  • Kilian Evang, Laura Kallmeyer, Jakub Waszczuk, Kilu von Prince, Tatiana Bladier, Simon Petitjean
  • TLDR: We propose a novel approach for parsing low-resource languages using role and reference grammar and self-training.
• A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning
  • Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
  • TLDR: We propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embeddents without semantic loss, thereby improving cross-lingual transferability.
• Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings
  • Linlin Liu, Thien Hai Nguyen, Shafiq Joty, Lidong Bing, Luo Si
  • TLDR: We propose a novel framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only.
• How to Parse a Creole: When Martinican Creole Meets French
  • Ludovic Mompelat, Daniel Dakota, Sandra Kübler
  • TLDR: We investigate methods to develop a parser for Martinican Creole, a highly under-resourced language, using a French treebank.
• Byte-based Multilingual NMT for Endangered Languages
  • Mengjiao Zhang, Jia Xu
  • TLDR: Byte-based multilingual neural machine translation system for endangered languages.
• BRCC and SentiBahasaRojak: The First Bahasa Rojak Corpus for Pretraining and Sentiment Analysis Dataset
  • Nanda Putri Romadhona, Sin-En Lu, Bo-Han Lu, Richard Tzong-Han Tsai
  • TLDR: We develop a new pre-trained model for code-mixing in Bahasa Rojak and use it to deal with the code-mixed input.
• WordNet-QU: Development of a Lexical Database for Quechua Varieties
  • Nelsi Melgarejo, Rodolfo Zevallos, Hector Gomez, John E. Ortega
  • TLDR: We propose a lexical database for Quechua which is a new lexical resource for the language.
• When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT
  • Pranaydeep Singh, Els Lefever
  • TLDR: We present a method to distill monolingual models from a jointly trained model for 102 languages (mBERT) and show that it is possible for the target language to outperform the original model, even with a basic distillation setup.
• Zero-shot Disfluency Detection for Indian Languages
  • Rohit Kundu, Preethi Jyothi, Pushpak Bhattacharyya
  • TLDR: We propose a novel method for zero-shot disfluency detection in Indian languages using synthetic disfluencies generated by a pretrained multilingual model.
• Evaluating Word Embeddings in Extremely Under-Resourced Languages: A Case Study in Bribri
  • Rolando Coto-Solano
  • TLDR: We use word embeddings to measure semantic and morphological learning in Bribri, a Chibchan language from Costa Rica.
• Applying Natural Annotation and Curriculum Learning to Named Entity Recognition for Under-Resourced Languages
  • Valeriy Lobov, Alexandra Ivoylova, Serge Sharoff
  • TLDR: We show that using natural annotation to build synthetic training sets from resources not initially designed for the target downstream task can improve the performance of the models trained on the original set.
• Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation
  • Xiaolin Xing, Yu Hong, Minhan Xu, Jianmin Yao, Guodong Zhou
  • TLDR: We propose a bidirectionally-adaptive learning strategy for low-resource neural machine translation.
• HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System
  • Zhanyu Ma, Jian Ye, Xurui Yang, Jianfeng Liu
  • TLDR: We propose a hierarchical framework to classify the pre-defined intents in the high-level and fulfill slot filling under the guidance of intent in the low-level.
• GraDA: Graph Generative Data Augmentation for Commonsense Reasoning
  • Adyasha Maharana, Mohit Bansal
  • TLDR: Graph-based generative data augmentation for commonsense reasoning.
• Eureka: Neural Insight Learning for Knowledge Graph Reasoning
  • Alex X. Zhang, Xun Liang, Bo Wu, Xiangping Zheng, Sensen Zhang, Yuhui Guo, Jun Wang, Xinyao Liu
  • TLDR: We propose a novel insight learning framework for knowledge graph reasoning that learns unseen relations from only one trigger event.
• CitRet: A Hybrid Model for Cited Text Span Retrieval
  • Amit Pandey, Avani Gupta, Vikram Pudi
  • TLDR: We present a novel hybrid model for cited text span retrieval that leverages unique semantic and syntactic structural characteristics of scientific documents.
• A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions
  • Arpita Kundu, Subhasish Ghosh, Pratik Saini, Tapas Nayak, Indrajit Bhattacharya
  • TLDR: We present a dataset of interview questions with difficulty scores for deep learning and use it to evaluate SOTA models for question difficulty prediction trained using weak supervision.
• Reinforcement Learning with Large Action Spaces for Neural Machine Translation
  • Asaf Yehudai, Leshem Choshen, Lior Fox, Omri Abend
  • TLDR: We propose a new method for improving neural machine translation performance by reducing the size of the vocabulary and improving the depth of the action space.
• Noise Learning for Text Classification: A Benchmark
  • Bo Liu, Wandi Xu, Yuejia Xiang, Xiaojun Wu, Lejian He, Bowen Zhang, Li Zhu
  • TLDR: We present a new benchmark of noise learning for text classification and show that the commonly-used datasets contain 0.61% to 15.77% noise labels which can cause inaccurate evaluation.
• Mitigating the Diminishing Effect of Elastic Weight Consolidation
  • Canasai Kruengkrai, Junichi Yamagishi
  • TLDR: We propose two objective functions to mitigate catastrophic forgetting in sequential training by rescaling the components of EWC.
• Token and Head Adaptive Transformers for Efficient Natural Language Processing
  • Chonghan Lee, Md Fahim Faysal Khan, Rita Brugarolas Brufau, Ke Ding, Vijaykrishnan Narayanan
  • TLDR: We propose to compress and accelerate various BERT-based models via simple fine-tuning and optimize their efficiency under limited computational resources.
• Don’t Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention Pooling
  • Dongsuk Oh, Yejin Kim, Hodong Lee, H. Howie Huang, Heuiseok Lim
  • TLDR: We present a novel approach to learn digested linguistic features from pre-trained language models by preserving layer-wise signals captured in each layer and learning digested features for downstream tasks.
• SHAP-Based Explanation Methods: A Review for NLP Interpretability
  • Edoardo Mosca, Ferenc Szigeti, Stella Tragianni, Daniel Gallagher, Georg Groh
  • TLDR: Model explanations are crucial for the transparent, safe, and trustworthy deployment of machine learning models.
• A Simple Log-based Loss Function for Ordinal Text Classification
  • François Castagnos, Martin Mihelich, Charles Dognin
  • TLDR: We propose a new simple loss function for ordinal text classification that outperforms state-of-the-art loss functions on four benchmark text classification datasets.
• Ask Question First for Enhancing Lifelong Language Learning
  • Han Wang, Ruiliu Fu, Xuejun Zhang, Jun Zhou, Qingwei Zhao
  • TLDR: We propose a new data format for lifelong language learning that allows the model to generate pseudo data that match corresponding tasks, and a new training task to train pseudo questions of previous tasks.
• DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification
  • Hui Chen, Wei Han, Diyi Yang, Soujanya Poria
  • TLDR: We propose a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification.
• Large Sequence Representation Learning via Multi-Stage Latent Transformers
  • Ionut-Catalin Sandu, Daniel Voinea, Alin-Ionut Popa
  • TLDR: We present LANTERN, a multi-stage transformer architecture for named-entity recognition (NER) designed to operate on indefinitely large text sequences (i.e. > 512 elements).
• MockingBERT: A Method for Retroactively Adding Resilience to NLP Models
  • Jan Jezabek, Akash Singh
  • TLDR: We propose a novel method of retroactively adding resilience to misspellings to adversarial adversarial attacks to transformer-based NLP models.
• Equivariant Transduction through Invariant Alignment
  • Jennifer C. White, Ryan Cotterell
  • TLDR: We present a novel group-equivariant neural network that incorporates a group-invariant hard alignment mechanism and show that it outperforms previous group-exivariant networks empirically on the SCAN task.
• Where Does Linguistic Information Emerge in Neural Language Models? Measuring Gains and Contributions across Layers
  • Jenny Kunz, Marco Kuhlmann
  • TLDR: We propose a new metric for probing neural language models that explicitly models local information gain relative to the previous layer and each layer’s contribution to the model’S overall performance.
• Accelerating Inference for Pretrained Language Models by Unified Multi-Perspective Early Exiting
  • Jun Kong, Jin Wang, Liang-Chih Yu, Xuejie Zhang
  • TLDR: Unified horizontal and vertical multi-perspective early exiting algorithm for PLMs.
• Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection
  • Li Gao, Lingyun Song, Jie Liu, Bolin Chen, Xuequn Shang
  • TLDR: We propose a novel topology imbalance smoothing strategy for graph convolutional learning and a hierarchical-level attention mechanism for graph Convolutional Learning.
• Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding
  • Linhai Zhang, Deyu Zhou
  • TLDR: We propose a novel TKGC method, TKG-AGP, by mapping the entities and relations in TKGs to the approximations of multivariate Gaussian processes (MGPs).
• CILDA: Contrastive Data Augmentation Using Intermediate Layer Knowledge Distillation
  • Md Akmal Haidar, Mehdi Rezagholizadeh, Abbas Ghaddar, Khalil Bibi, Phillippe Langlais, Pascal Poupart
  • TLDR: We propose a learning-based data augmentation technique for knowledge distillation based on intermediate layer matching using contrastive loss to improve masked adversarial data augmentation.
• Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher
  • Mehdi Rezagholizadeh, Aref Jafari, Puneeth S.M. Saladi, Pranav Sharma, Ali Saheb Pasand, Ali Ghodsi
  • TLDR: We show that the best performing checkpoint of the teacher might not necessarily be the best teacher for training the student in knowledge distillation.
• Classical Sequence Match Is a Competitive Few-Shot One-Class Learner
  • Mengting Hu, Hang Gao, Yinhao Bai, Mingming Liu
  • TLDR: We propose a new few-shot sequence match method that is more efficient than the classical methods.
• Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning
  • Nghia Ngo Trung, Linh Ngo Van, Thien Huu Nguyen
  • TLDR: Meta-learning framework for unsupervised domain adaptation for text classification.
• WARM: A Weakly (+Semi) Supervised Math Word Problem Solver
  • Oishik Chatterjee, Isha Pandey, Aashish Waikar, Vishwajeet Kumar, Ganesh Ramakrishnan
  • TLDR: We propose a weakly supervised model for solving math word problems by requiring only the final answer as supervision.
• Attention Networks for Augmenting Clinical Text with Support Sets for Diagnosis Prediction
  • Paul Grundmann, Tom Oberhauser, Felix Gers, Alexander Löser
  • TLDR: We propose to augment clinical text with support sets of diagnostic codes from previous admissions or as they emerge during differential diagnosis.
• PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks
  • Pengwei Zhan, Chao Zheng, Jing Yang, Yuxiang Wang, Liming Wang, Yang Wu, Yunjian Zhang
  • TLDR: We propose PAthological woRd Saliency sEarch (PARSE) that performs the search under dynamic search space following the subarea importance.
• A Closer Look at Parameter Contributions When Training Neural Language and Translation Models
  • Raúl Vázquez, Hande Celikkanat, Vinit Ravishankar, Mathias Creutz, Jörg Tiedemann
  • TLDR: We analyze the learning dynamics of neural language and translation models using Loss Change Allocation, an indicator that enables a fine-grained analysis of parameter updates when optimizing for the loss function.
• KNOT: Knowledge Distillation Using Optimal Transport for Solving NLP Tasks
  • Rishabh Bhardwaj, Tushar Vaidya, Soujanya Poria
  • TLDR: We propose a new approach, Knowledge Distillation using Optimal Transport (KNOT), to distill the natural language semantic knowledge from multiple teacher networks to a student network.
• An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning
  • Shaobin Chen, Jie Zhou, Yuling Sun, Liang He
  • TLDR: We present an information minimization based contrastive learning Infor sentence embeddings learning model for unsupervised sentence representation learning.
• Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks
  • Siddhartha Datta
  • TLDR: We propose a new type of black-box adversarial attack for NLP systems that can transfer adversarial examples to target domain and cause poor performance in target model.
• Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification
  • Suhe Wang, Xiaoyuan Liu, Bo Liu, Diwen Dong
  • TLDR: We present a novel few-shot classification framework based on adversarial meta-learning and explore the attention mechanism to promote more comprehensive feature expression.
• Reweighting Strategy Based on Synthetic Data Identification for Sentence Similarity
  • TaeHee Kim, ChaeHun Park, Jimin Hong, Radhika Dua, Edward Choi, Jaegul Choo
  • TLDR: We propose a novel approach that first trains the classifier to measure the importance of each sentence and then uses it to train a sentence embedding model.
• MaxMatch-Dropout: Subword Regularization for WordPiece
  • Tatsuya Hiraoka
  • TLDR: We present a subword regularization method for WordPiece, which uses a maximum matching algorithm for tokenization.
• Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification
  • Tianyi Lei, Honghui Hu, Qiaoyang Luo, Dezhong Peng, Xu Wang
  • TLDR: We propose a novel Adaptive Meta-learner via Gradient Similarity method to improve the model generalization ability to a new task.
• Vocabulary-informed Language Encoding
  • Xi Ai, Bin Fang
  • TLDR: We present a method to compute a vocabulary-informed language encoding as the language representation, for a required language, considering a local vocabulary covering an acceptable amount of the most frequent word embeddings in this language.
• OpticE: A Coherence Theory-Based Model for Link Prediction
  • Xiangyu Gui, Feng Zhao, Langjunqing Jin, Hai Jin
  • TLDR: We propose a new embedding approach for knowledge graph knowledge representation learning based on optical interference and a novel negative sampling method for learning semantic similarity.
• Smoothed Contrastive Learning for Unsupervised Sentence Embedding
  • Xing Wu, Chaochen Gao, Yipeng Su, Jizhong Han, Zhongyuan Wang, Songlin Hu
  • TLDR: We propose a simple smoothing strategy for unsupervised contrastive sentence embedding models that improves performance on semantic text similarity tasks.
• Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model Compression
  • Xinge Ma, Jin Wang, Liang-Chih Yu, Xuejie Zhang
  • TLDR: We propose a knowledge distillation method with reptile meta-learning to facilitate the transfer of knowledge from the teacher to the student.
• RotateCT: Knowledge Graph Embedding by Rotation and Coordinate Transformation in Complex Space
  • Yao Dong, Lei Wang, Ji Xiang, Xiaobo Guo, Yuqiang Xie
  • TLDR: We propose a new knowledge graph embedding method called RotateCT, which first transforms the coordinates of each entity, and then represents each relation as a rotation from head entity to tail entity in complex space.
• Can Data Diversity Enhance Learning Generalization?
  • Yu Yu, Shahram Khadivi, Jia Xu
  • TLDR: We show that the diversification of training samples alleviates overfitting and improves model generalization and accuracy.
• Generate-and-Retrieve: Use Your Predictions to Improve Retrieval for Semantic Parsing
  • Yury Zemlyanskiy, Michiel de Jong, Joshua Ainslie, Panupong Pasupat, Peter Shaw, Linlu Qiu, Sumit Sanghai, Fei Sha
  • TLDR: We propose GandR, a retrieval procedure that retrieves exemplars for which outputs are also similar.
• Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding
  • Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang, Yutao Zhu, Zheng Liu, Jiawen Wu, Dejiang Kong, Ruofei Lai, Zhao Cao, Zhicheng Dou, Xipeng Qiu
  • TLDR: We propose a hierarchical framework for learning downstream tasks by analyzing the task correlation and propose a novel model to learn basic language properties from all tasks.
• Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models
  • Zichun Yu, Tianyu Gao, Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Maosong Sun, Jie Zhou
  • TLDR: We propose AutoSeq, a fully automatic prompting method for sequence-to-sequence models, enabling free-form generation and larger label search space; the generated label sequences are even better than curated manual ones on a variety of tasks.
• Unsupervised Sentence Textual Similarity with Compositional Phrase Semantics
  • Zihao Wang, Jiaheng Dou, Yong Zhang
  • TLDR: We propose a novel algorithm for unsupervised sentence text similarity computation that is faster than existing approaches and is empirically more effective and scalable than previous approaches.
• A Generalized Method for Automated Multilingual Loanword Detection
  • Abhijnan Nath, Sina Mahdipour Saravani, Ibrahim Khebour, Sheikh Mannan, Zihui Li, Nikhil Krishnaswamy
  • TLDR: We present a method to automatically detect loanwords across language pairs, accounting for differences in script, pronunciation and phonetic transformation by the borrowing language.
• FeatureBART: Feature Based Sequence-to-Sequence Pre-Training for Low-Resource NMT
  • Abhisek Chakrabarty, Raj Dabre, Chenchen Ding, Hideki Tanaka, Masao Utiyama, Eiichiro Sumita
  • TLDR: Feature-based sequence-to-sequence monolingual pre-training strategy for low-resource NMT.
• Multi-level Community-awareness Graph Neural Networks for Neural Machine Translation
  • Binh Nguyen, Long Nguyen, Dien Dinh
  • TLDR: We propose a novel Multi-level Community-awareness Graph Neural Network (MC-GNN) layer to jointly model local and global relationships between words and their linguistic roles in multiple communities.
• On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation
  • Changtong Zan, Liang Ding, Li Shen, Yu Cao, Weifeng Liu, Dacheng Tao
  • TLDR: We show that pre-training of text representations on resource-rich NMT could be nicely complementary to its Random-Initialization counterpart, achieving substantial improvements considering both translation accuracy, generalization, and negative diversity.
• ngram-OAXE: Phrase-Based Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation
  • Cunxiao Du, Zhaopeng Tu, Longyue Wang, Jing Jiang
  • TLDR: We extend oaxe by allowing reordering between ngram phrases and still requiring a strict match of word order within the phrases.
• Language Branch Gated Multilingual Neural Machine Translation
  • Haoran Sun, Deyi Xiong
  • TLDR: Language branch gated multilingual neural machine translation that encourages knowledge transfer across languages.
• Iterative Constrained Back-Translation for Unsupervised Domain Adaptation of Machine Translation
  • Hongxiao Zhang, Hui Huang, Jiale Gao, Yufeng Chen, Jinan Xu, Jian Liu
  • TLDR: Iterative Constrained Back-Translation for NMT.
• Linguistically-Motivated Yorùbá-English Machine Translation
  • Ife Adebara, Muhammad Abdul-Mageed, Miikka Silfverberg
  • TLDR: We analyze how well the Transformer and SMT systems translate bare nouns in Yorùbá into English.
• Dynamic Position Encoding for Transformers
  • Joyce Zheng, Mehdi Rezagholizadeh, Peyman Passban
  • TLDR: We propose a novel architecture with new position embeddings that take the order of the target words into consideration to improve neural machine translation.
• PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation
  • Juncheng Wan, Jian Yang, Shuming Ma, Dongdong Zhang, Weinan Zhang, Yong Yu, Zhoujun Li
  • TLDR: We propose a phrase-level adversarial example generation framework for neural machine translation that improves robustness to noise.
• Noise-robust Cross-modal Interactive Learning with Text2Image Mask for Multi-modal Neural Machine Translation
  • Junjie Ye, Junjun Guo, Yan Xiang, Kaiwen Tan, Zhengtao Yu
  • TLDR: We propose a novel multi-modal interactive fusion approach for multi-mode neural machine translation in the presence of text-related images.
• Speeding up Transformer Decoding via an Attention Refinement Network
  • Kaixin Wu, Yue Zhang, Bojie Hu, Tong Zhang
  • TLDR: We propose a lightweight attention structure called Attention Refinement Network (ARN) for speeding up Transformer.
• Interactive Post-Editing for Verbosity Controlled Translation
  • Prabhakar Gupta, Anil Nelakanti, Grant M. Berry, Abhishek Sharma
  • TLDR: We explore Interactive Post-Editing (IPE) models for human-in-loop translation to help correct translation errors and rephrase it with a desired style variation.
• Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering
  • Qian Wang, Jiajun Zhang
  • TLDR: We propose a fuzzy task clustering method for multilingual neural machine translation that can handle the asymmetric problem in multilingual NMT.
• Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation
  • Qiang Wang, Rongxiang Weng, Ming Chen
  • TLDR: We propose a novel approach to retrieval of external corpus from external corpus by using the query vector of the translation task as the query vectors of the retrieval task.
• Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language Model
  • Qiao Cheng, Jin Huang, Yitao Duan
  • TLDR: We present a new data augmentation method for neural machine translation that can enforce stronger semantic consistency both within and across languages.
• Informative Language Representation Learning for Massively Multilingual Neural Machine Translation
  • Renren Jin, Deyi Xiong
  • TLDR: Language embedding embodiment and language-aware multi-head attention for multilingual neural machine translation.
• Rare but Severe Neural Machine Translation Errors Induced by Minimal Deletion: An Empirical Study on Chinese and English
  • Ruikang Shi, Alvin Grissom II, Duc Minh Trinh
  • TLDR: We show that deleting a single character in a character-based model can induce severe translation errors.
• QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation
  • Sugyeong Eo, Chanjun Park, Hyeonseok Moon, Jaehyung Seo, Gyeongmin Kim, Jungseob Lee, Heuiseok Lim
  • TLDR: We present a synthetic synthetic synthetic QE dataset generated in a fully automatic manner that can be scaled up to 1.58M.
• Improving Both Domain Robustness and Domain Adaptability in Machine Translation
  • Wen Lai, Jindřich Libovický, Alexander Fraser
  • TLDR: We propose a novel meta-learning framework for neural machine translation domain adaptation using meta-learners.
• CoDoNMT: Modeling Cohesion Devices for Document-Level Neural Machine Translation
  • Yikun Lei, Yuqi Ren, Deyi Xiong
  • TLDR: We propose a document-level neural machine translation framework, CoDoNMT, which models cohesion devices from two perspectives: Cohesion Device Masking (CoDM) and Cohesion Attention Focusing (CoAF).
• Improving Non-Autoregressive Neural Machine Translation via Modeling Localness
  • Yong Wang, Xinwei Geng
  • TLDR: We propose a novel method for improving the localness of non-autoregressive translation models by incorporating information about surrounding words.
• Categorizing Semantic Representations for Neural Machine Translation
  • Yongjing Yin, Yafu Li, Fandong Meng, Jie Zhou, Yue Zhang
  • TLDR: We propose a novel method for compositional generalization of neural machine translation models by introducing categorization to the source contextualized representations.
• Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation
  • Yuto Kuroda, Tomoyuki Kajiwara, Yuki Arase, Takashi Ninomiya
  • TLDR: We propose a method to distill language-agnostic meaning embeddings from multilingual sentence encoders for unsupervised quality estimation of machine translation.
• Alleviating the Inequality of Attention Heads for Neural Machine Translation
  • Zewei Sun, Shujian Huang, Xinyu Dai, Jiajun Chen
  • TLDR: We propose a simple masking method for Transformer that improves translation on multiple language pairs.
• Adapting to Non-Centered Languages for Zero-shot Multilingual Translation
  • Zhi Qu, Taro Watanabe
  • TLDR: We propose a language-specific modeling method for zero-shot translation that can counteract the instability of zero-shots translation.
• Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training
  • Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su
  • TLDR: We propose an iterative data-switch training framework for robust neural machine translation that can exploit just one type of examples at each single stage, which can better exploit authentic and adversarial examples, and thus obtaining a better robust NMT model.
• Cross-lingual Feature Extraction from Monolingual Corpora for Low-resource Unsupervised Bilingual Lexicon Induction
  • Zihao Feng, Hailong Cao, Tiejun Zhao, Weixuan Wang, Wei Peng
  • TLDR: We propose a cross-lingual feature extraction method for unsupervised bilingual lexicon induction on low-resource distant language pairs.
• Language-Independent Approach for Morphological Disambiguation
  • Alymzhan Toleu, Gulmira Tolegen, Rustam Mussabayev
  • TLDR: Language-independent approach for morphological disambiguation.
• SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
  • Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li
  • TLDR: We propose a data uncertainty constraint to improve the performance of text-to-SQL parsing by exploring the underlying complementary semantic information among multiple semantically-equivalent questions (many-to one) and learn the robust feature representations with reduced spurious associations.
• Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages
  • Georgie Botev, Arya D. McCarthy, Winston Wu, David Yarowsky
  • TLDR: We present a detailed empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks.
• PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling
  • Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu
  • TLDR: We propose a perturbed semantic structure awareness transferring method for training perturbation-robust slot filling models.
• String Editing Based Chinese Grammatical Error Diagnosis
  • Haihua Xie, Xiaoqing Lyu, Xuefei Chen
  • TLDR: We propose a string editing based CGED model that requires less training data by using a unified workflow to handle various types of grammatical errors.
• The Fragility of Multi-Treebank Parsing Evaluation
  • Iago Alonso-Alonso, David Vilares, Carlos Gómez-Rodríguez
  • TLDR: We show that evaluating on a single subset of treebanks can lead to weak conclusions.
• FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition
  • Linyi Yang, Lifan Yuan, Leyang Cui, Wenyang Gao, Yue Zhang
  • TLDR: We propose a rationale-centric data augmentation method for few-shot named entity recognition that improves the generalization ability of few-shots NER.
• Speaker-Aware Discourse Parsing on Multi-Party Dialogues
  • Nan Yu, Guohong Fu, Min Zhang
  • TLDR: We present a speaker-aware model for discourse parsing on multi-party dialogues using the interaction features between different speakers.
• Iterative Span Selection: Self-Emergence of Resolving Orders in Semantic Role Labeling
  • Shuhei Kurita, Hiroki Ouchi, Kentaro Inui, Satoshi Sekine
  • TLDR: We propose a novel algorithm for semantic role labeling that learns the optimal labeling order for semantic arguments.
• Revisiting the Practical Effectiveness of Constituency Parse Extraction from Pre-trained Language Models
  • Taeuk Kim
  • TLDR: We show that constituency parse trees generated by pre-trained language models can be competitive with common unsupervised parsers in few-shot settings.
• Position Offset Label Prediction for Grammatical Error Correction
  • Xiuyu Wu, Jingsong Yu, Xu Sun, Yunfang Wu
  • TLDR: We propose a novel position offset label prediction subtask to the encoder-decoder architecture for grammatical error correction (GEC) task, which is naturally capable of integrating different correction editing operations into a unified framework.
• Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning
  • Xuantao Lu, Jingping Liu, Zhouhong Gu, Hanwen Tong, Chenhao Xie, Junyang Huang, Yanghua Xiao, Wenguang Wang
  • TLDR: We propose a scoring model to automatically learn a model-based reward for semantic parsing and a curriculum learning strategy to stabilize the training process.
• Yet Another Format of Universal Dependencies for Korean
  • Yige Chen, Eunkyul Leah Jo, Yundong Yao, KyungTae Lim, Miikka Silfverberg, Francis M. Tyers, Jungyeul Park
  • TLDR: We propose a morpheme-based scheme for Korean dependency parsing and adopt the proposed scheme to Universal Dependencies.
• Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing
  • Yuanhe Tian, Yan Song, Fei Xia
  • TLDR: We propose a novel approach to improve graph-based dependency parsing by pre-training on auto-parsed data and fine-tuning on gold dependency trees.
• Simple and Effective Graph-to-Graph Annotation Conversion
  • Yuxuan Wang, Zhilin Lei, Yuqiu Ji, Wanxiang Che
  • TLDR: We propose two simple and effective graph-to-graph annotation conversion approaches, namely Label Switching and Graph2Graph Linear Transformation, which use pseudo data and inherit parameters to guide graph conversions respectively.
• BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning
  • Ziming Cheng, Zuchao Li, Hai Zhao
  • TLDR: We propose data-efficient Bidirectional Bayesian Bayesian learning for meaning representation and text transformation.
• Multi-Layer Pseudo-Siamese Biaffine Model for Dependency Parsing
  • Ziyao Xu, Houfeng Wang, Bingdong Wang
  • TLDR: We propose a multi-layer pseudo-Siamese biaffine model for neural dependency parsing.
• Belief Revision Based Caption Re-ranker with Visual Semantic Information
  • Ahmed Sabir, Francesc Moreno-Noguer, Pranava Madhyastha, Lluís Padró
  • TLDR: We propose a novel re-ranking approach that leverages visual-semantic measures to identify the ideal caption that maximally captures the visual information in the image.
• Towards Understanding the Relation between Gestures and Language
  • Artem Abzaliev, Andrew Owens, Rada Mihalcea
  • TLDR: We explore the relation between gestures and language and show that gestures are predictive of the native language of the speaker, and that gesture embeddings further improve language prediction result.
• Building Joint Relationship Attention Network for Image-Text Generation
  • Changzhi Wang, Xiaodong Gu
  • TLDR: We propose a new relationship based attention based method for image-text generation that learns two relationships among image features and adaptively adapts the output relationship representation when predicting different words.
• Learning to Focus on the Foreground for Temporal Sentence Grounding
  • Daizong Liu, Wei Hu
  • TLDR: We propose a novel framework for temporal sentence grounding with learnable foregrounds that captures fine-grained foreground information and improves the quality of the detection model.
• Are Visual-Linguistic Models Commonsense Knowledge Bases?
  • Hsiu-Yu Yang, Carina Silberer
  • TLDR: We show that visual-linguistic models are highly promising for natural language understanding tasks that require commonsense knowledge.
• Visual Prompt Tuning for Few-Shot Text Classification
  • Jingyuan Wen, Yutian Luo, Nanyi Fei, Guoxing Yang, Zhiwu Lu, Hao Jiang, Jie Jiang, Zhao Cao
  • TLDR: We propose a novel method for deploying vision-language pre-training models in few-shot text classification task.
• Systematic Analysis of Image Schemas in Natural Language through Explainable Multilingual Neural Language Processing
  • Lennart Wachowiak, Dagmar Gromann
  • TLDR: We propose a new neural model for automatic classification of image schemas in natural language.
• How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?
  • Lovisa Hagström, Richard Johansson
  • TLDR: We investigate and compare seven possible methods for adapting three different pre-trained multimodal vision-and-language models to text-only input without out-of-distribution uncertainty.
• ACT-Thor: A Controlled Benchmark for Embodied Action Understanding in Simulated Environments
  • Michael Hanna, Federico Pedeni, Alessandro Suglia, Alberto Testoni, Raffaella Bernardi
  • TLDR: We present ACT-Thor, a novel controlled benchmark for embodied action understanding, which provides a new way of evaluating embodied AI agents that understand grounded actions.
• In-the-Wild Video Question Answering
  • Santiago Castro, Naihao Deng, Pingxuan Huang, Mihai Burzo, Rada Mihalcea
  • TLDR: We propose WILDQA, a video understanding dataset of videos recorded in outside settings.
• Towards Better Semantic Understanding of Mobile Interfaces
  • Srinivas Sunkara, Maria Wang, Lijuan Liu, Gilles Baechler, Yu-Chung Hsiao, Jindong Chen, Abhanshu Sharma, James W. W. Stout
  • TLDR: We present a new dataset of mobile UI elements with a large number of unique annotations and a new model for visualizing UI elements.
• End-to-end Dense Video Captioning as Sequence Generation
  • Wanrong Zhu, Bo Pang, Ashish V. Thapliyal, William Yang Wang, Radu Soricut
  • TLDR: We show how to model the two subtasks of dense video captioning jointly as one sequence generation task, and simultaneously predict the events and the corresponding descriptions.
• SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning
  • Wei Han, Hui Chen, Zhen Hai, Soujanya Poria, Lidong Bing
  • TLDR: We propose SANCL: Selective Attention and Natural Contrastive Learning for MRHP.
• Dual Capsule Attention Mask Network with Mutual Learning for Visual Question Answering
  • Weidong Tian, Haodong Li, Zhong-Qiu Zhao
  • TLDR: We propose a novel dual capsule attention mask network for Visual Question Answering that combines coarse-grained and fine-grain information for VQA.
• Emergence of Hierarchical Reference Systems in Multi-agent Communication
  • Xenia Ohmer, Marko Duda, Elia Bruni
  • TLDR: We develop a novel communication game, the hierarchical reference game, to study the emergence of such reference systems in artificial agents.
• Scene Graph Modification as Incremental Structure Expanding
  • Xuming Hu, Zhijiang Guo, Yu Fu, Lijie Wen, Philip S. Yu
  • TLDR: We propose a novel method for graph modification based on incremental structure expanding and a novel query-based model for scene graph modification.
• Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances
  • Yike Wu, Yu Zhao, Shiwan Zhao, Ying Zhang, Xiaojie Yuan, Guoqing Zhao, Ning Jiang
  • TLDR: We propose a novel training framework that explicitly encourages the VQA model to distinguish between the superficially similar instances.
• Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss
  • Youhan Lee, KyungTae Lim, Woonhyuk Baek, Byungseok Roh, Saehoon Kim
  • TLDR: We propose a simple yet effective enhancement scheme for previous multilingual multi-modal representation methods by using a limited number of pairs of images and non-English texts.
• LOViS: Learning Orientation and Visual Signals for Vision and Language Navigation
  • Yue Zhang, Parisa Kordjamshidi
  • TLDR: We design a neural agent with explicit Orientation and Vision modules for navigation that learns to ground spatial and visual information and use it to ground landmark mentions in the instructions to the visual environment more effectively.
• GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation
  • Anthony Colas, Mehrdad Alvandipour, Daisy Zhe Wang
  • TLDR: We propose a graph-aware graph-to-text generation algorithm that outperforms state-of-the-art models and closes the gap imposed by additional pre-training tasks.
• Content Type Profiling of Data-to-Text Generation Datasets
  • Ashish Upadhyay, Stewart Massie
  • TLDR: We propose a novel typology of content types, that we use to classify the contents of event summaries.
• CoLo: A Contrastive Learning Based Re-ranking Framework for One-Stage Summarization
  • Chenxin An, Ming Zhong, Zhiyong Wu, Qin Zhu, Xuanjing Huang, Xipeng Qiu
  • TLDR: We propose a Contrastive Learning based re-ranking framework for one-stage summarization which improves the extractive and abstractive results of one-stages systems on CNN/DailyMail benchmark by 3x 8x speed-up ratio during inference while maintaining comparable results.
• Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation
  • Cyril Chhun, Pierre Colombo, Fabian M. Suchanek, Chloé Clavel
  • TLDR: We propose to re-evaluate the metrics for automatic story generation and show how well they correlate with human evaluation criteria.
• Selective Token Generation for Few-shot Natural Language Generation
  • Daejin Jo, Taehwan Kwon, Eun-Sol Kim, Sungwoong Kim
  • TLDR: We propose a novel additive learning algorithm based on reinforcement learning that selectively outputs language tokens between the task-general PLM and the task specific adapter during both training and inference.
• A-TIP: Attribute-aware Text Infilling via Pre-trained Language Model
  • Dongyuan Li, Jingyi You, Kotaro Funakoshi, Manabu Okumura
  • TLDR: We propose a novel attribute-aware text infilling method that improves attribute relevance without decreasing text fluency.
• Multi Graph Neural Network for Extractive Long Document Summarization
  • Xuan-Dung Doan, Le-Minh Nguyen, Khac-Hoai Nam Bui
  • TLDR: We present a novel method for extracting document summarization by exploiting the cross-relations between words and sentences.
• Improving Zero-Shot Multilingual Text Generation via Iterative Distillation
  • Ernie Chang, Alex Marin, Vera Demberg
  • TLDR: We propose a novel method to generate synthetic multilingual text from the training data and pretrained teacher models.
• Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation
  • Fei-Tzin Lee, Miguel Ballesteros, Feng Nan, Kathleen McKeown
  • TLDR: We show that syntactic control tags can be reliably controlled in pretrained language models, even in settings that diverge drastically from the training distribution.
• PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment
  • Ge Luo, Hebi Li, Youbiao He, Forrest Sheng Bao
  • TLDR: We propose to evaluate machine-generated summaries without a human-written reference summary by learning the preference rank of summaries using the Bradley-Terry power ranking model from inferior summaries generated by corrupting base summaries.
• Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator
  • Guisheng Liu, Yi Li, Yanqing Guo, Xiangyang Luo, Bo Wang
  • TLDR: We propose a new framework for multi-attribute controlled text generation that achieves remarkable controllability in multi-attribution generation while keeping the text fluent and diverse.
• Coordination Generation via Synchronized Text-Infilling
  • Hiroki Teranishi, Yuji Matsumoto
  • TLDR: We propose a simple yet effective approach to generate sentences with a coordinate structure in which the boundaries of its conjuncts are explicitly specified.
• KHANQ: A Dataset for Generating Deep Questions in Education
  • Huanli Gong, Liangming Pan, Hengchang Hu
  • TLDR: We present KHANQ, a challenging dataset for educational question generation, containing 1,034 high-quality learner-generated questions seeking an in-depth understanding of the taught online courses in Khan Academy.
• Multi-Figurative Language Generation
  • Huiyuan Lai, Malvina Nissim
  • TLDR: We provide a benchmark for the automatic generation of five common figurative forms in English and provide a mechanism for injecting the target figurative information into the encoder.
• Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation
  • Husam Quteineh, Spyridon Samothrakis, Richard Sutcliffe
  • TLDR: We present a novel approach where knowledge can be distilled from a teacher model to a student model through the generation of synthetic data.
• Boosting Code Summarization by Embedding Code Structures
  • Jikyoeng Son, Joonghyuk Hahn, HyeonTae Seo, Yo-Sub Han
  • TLDR: We propose a graph embedding method for summarizing source code that improves the performance of existing models and its robustness.
• Comparative Graph-based Summarization of Scientific Papers Guided by Comparative Citations
  • Jingqiang Chen, Chaoxiang Cai, Xiaorui Jiang, Kejia Chen
  • TLDR: We propose a method to generate comparative summaries using citations as guidance.
• JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation
  • Jingyi You, Dongyuan Li, Manabu Okumura, Kenji Suzuki
  • TLDR: Jointly learning framework for automated disease Prediction and radiology report generation.
• Automatic Nominalization of Clauses
  • John S. Y. Lee, Ho Hung Lim, Carol Webster, Anton Melser
  • TLDR: We propose a novel method for clause nominalization that uses a textual entailment model to predict the word positions and word forms of the nominal modifiers.
• Benchmarking Compositionality with Formal Languages
  • Josef Valvoda, Naomi Saphra, Jonathan Rawski, Adina Williams, Ryan Cotterell
  • TLDR: We explore which of their properties contribute to learnability of a compositional relation by a neural network.
• Source-summary Entity Aggregation in Abstractive Summarization
  • José Ángel González, Annie Louis, Jackie Chi Kit Cheung
  • TLDR: In a text, entities mentioned earlier can be referred to in later discourse by a more general description.
• How to Find Strong Summary Coherence Measures? A Toolbox and a Comparative Study for Summary Coherence Measure Evaluation
  • Julius Steen, Katja Markert
  • TLDR: We present a large-scale investigation of various methods for automatic coherence evaluation of summaries and propose new analysis measures for coherence measures.
• Summarizing Dialogues with Negative Cues
  • Junpeng Liu, Yanyan Zou, Yuxuan Xi, Shengjie Li, Mian Ma, Zhuoye Ding
  • TLDR: We propose to explicitly have the model perceive redundant parts of an input dialogue history as negative cues to drive the model to focus less on the unimportant information and also pay more attention to the salient pieces.
• ALEXSIS-PT: A New Resource for Portuguese Lexical Simplification
  • Kai North, Marcos Zampieri, Tharindu Ranasinghe
  • TLDR: We present a novel multi-candidate dataset for Brazilian Portuguese LS containing 9,605 candidate substitutions for 387 complex words.
• APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations
  • Katherine Atwell, Sabit Hassan, Malihe Alikhani
  • TLDR: We present the first discourse-aware style-transfer models that can effectively reduce offensiveness in Reddit text while preserving the meaning of the original text.
• View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond
  • Keli Xie, Dongchen He, Jiaxin Zhuang, Siyuan Lu, Zhongfeng Wang
  • TLDR: We present a 2D view of dialogue summarization based on a time-speaker perspective and propose a two-stream model for dialogue summarizing.
• Denoising Large-Scale Image Captioning from Alt-text Data Using Content Selection Models
  • Khyathi Raghavi Chandu, Piyush Sharma, Soravit Changpinyo, Ashish V. Thapliyal, Radu Soricut
  • TLDR: We propose a novel method for generating captions from text-based data that are more controllable and denoised than noisy data.
• Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases
  • Kun Zhang, Yunqi Qiu, Yuanzhuo Wang, Long Bai, Wei Li, Xuhui Jiang, Huawei Shen, Xueqi Cheng
  • TLDR: Meta-learning for complex question generation over knowledge bases.
• Graph-to-Text Generation with Dynamic Structure Pruning
  • Liang Li, Ruiying Geng, Bowen Li, Can Ma, Yinliang Yue, Binhua Li, Yongbin Li
  • TLDR: We propose a structure aware graph-to-text decoder and a dynamic graph pruning mechanism to improve graph-based graph- to-text decoding.
• Multi-Perspective Document Revision
  • Mana Ihori, Hiroshi Sato, Tomohiro Tanaka, Ryo Masumura
  • TLDR: We propose a novel multi-perspective document revision task that simultaneously handles seven perspectives to improve the readability and clarity of a document.
• A Survey of Automatic Text Summarization Using Graph Neural Networks
  • Marco Ferdinand Salchner, Adam Jatowt
  • TLDR: We provide an overview on the rapidly evolving approach of using GNNs for the task of automatic text summarization.
• Phrase-Level Localization of Inconsistency Errors in Summarization by Weak Supervision
  • Masato Takatsuka, Tetsunori Kobayashi, Yoshihiko Hayashi
  • TLDR: We propose a methodology for localizing inconsistency errors in summarization and show that it can improve the accuracy of existing weakly supervised methods.
• PoliSe: Reinforcing Politeness Using User Sentiment for Customer Care Response Generation
  • Mauajama Firdaus, Asif Ekbal, Pushpak Bhattacharyya
  • TLDR: We propose a novel method for generating polite responses based on sentiment and conversational history.
• Focus-Driven Contrastive Learning for Medical Question Summarization
  • Ming Zhang, Shuai Dou, Ziyang Wang, Yunfang Wu
  • TLDR: We propose a novel question focus-driven contrastive learning framework for medical question summarization, which captures question focus and captures sentence-level semantics.
• ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining
  • Mohamed Elaraby, Diane Litman
  • TLDR: We propose a novel method to capture the argumentative structure of legal documents by integrating argument role labeling into the summarization process.
• Semantic Overlap Summarization among Multiple Alternative Narratives: An Exploratory Study
  • Naman Bansal, Mousumi Akter, Shubhra Kanti Karmaker
  • TLDR: We introduce a novel NLP task for semantic overlap summarization which uses sentence-wise annotation to generate a single summary from multiple alternative narratives.
• Analyzing the Dialect Diversity in Multi-document Summaries
  • Olubusayo Olabisi, Aaron Hudson, Antonie Jetter, Ameeta Agrawal
  • TLDR: We present a novel dataset of dialect diverse tweets and human-written summaries generated from social media data and show that human-annotated summaries are better than system-generated summaries in terms of their ability to represent salient as well as diverse perspectives.
• Multi-Document Scientific Summarization from a Knowledge Graph-Centric View
  • Pancheng Wang, Shasha Li, Kunyuan Pang, Liangliang He, Dong Li, Jintao Tang, Ting Wang
  • TLDR: We present knowledge graphs for multi-document scientific summaries.
• Generation of Patient After-Visit Summaries to Support Physicians
  • Pengshan Cai, Fei Liu, Adarsha Bajracharya, Joe Sills, Alok Kapoor, Weisong Liu, Dan Berlowitz, David Levy, Richeek Pradhan, Hong Yu
  • TLDR: We present a new method for automatic generation of lay language after-visit summaries and show that it can convey the gist of clinical visits.
• HeterGraphLongSum: Heterogeneous Graph Neural Network with Passage Aggregation for Extractive Long Document Summarization
  • Tuan-Anh Phan, Ngoc-Dung Ngoc Nguyen, Khac-Hoai Nam Bui
  • TLDR: We propose a novel graph neural network model for long document summarization using graph-based methods.
• GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization
  • Qianqian Xie, Jimin Huang, Tulika Saha, Sophia Ananiadou
  • TLDR: We propose a novel graph contrastive topic enhanced language model for long document extractive summarization, which incorporates the graph contrastiveness of the graph domain and the gold summary into the pre-trained language model.
• PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification Data for Learning Enhanced Generation
  • Sedrick Scott Keh, Kevin Lu, Varun Gangal, Steven Y. Feng, Harsh Jhamtani, Malihe Alikhani, Eduard Hovy
  • TLDR: We propose PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation.
• Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
  • Seungone Kim, Se June Joo, Hyungjoo Chae, Chaehyeong Kim, Seung-won Hwang, Jinyoung Yeo
  • TLDR: We propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them.
• Type-dependent Prompt CycleQAG : Cycle Consistency for Multi-hop Question Generation
  • Seungyeon Lee, Minho Lee
  • TLDR: We propose a new type-dependent prompt cycleQAG (cyclic question-answer-generation) and a cycle consistency loss for multi-hop question generation.
• UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor
  • Shangqing Tu, Jifan Yu, Fangwei Zhu, Juanzi Li, Lei Hou, Jian-Yun Nie
  • TLDR: We propose a novel approach to evaluate document salience using perplexity and document and keyword perplexity.
• DISK: Domain-constrained Instance Sketch for Math Word Problem Generation
  • Tianyang Cao, Shuang Zeng, Xiaodan Xu, Mairgup Mansur, Baobao Chang
  • TLDR: We propose a neural model for generating MWP text from math equations.
• Context-Tuning: Learning Contextualized Prompts for Natural Language Generation
  • Tianyi Tang, Junyi Li, Wayne Xin Zhao, Ji-Rong Wen
  • TLDR: We present a simple yet powerful paradigm for learning language generation using pretrained language models.
• PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization
  • Xiaochen Liu, Yang Gao, Yu Bai, Jiawei Li, Yinan Hu, Heyan Huang, Boxing Chen
  • TLDR: We present a novel soft prompts architecture coupled with a prompt pre-training plus prompt fine-tuning paradigm, which is effective and tunes only extremely light parameters.
• Continuous Decomposition of Granularity for Neural Paraphrase Generation
  • Xiaodong Gu, Zhaowei Zhang, Sang-Woo Lee, Kang Min Yoo, Jung-Woo Ha
  • TLDR: We present continuous decomposition of granularity for neural paraphrase generation and a novel attention mask to encode granularity into attention.
• Paraphrase Generation as Unsupervised Machine Translation
  • Xiaofei Sun, Yufei Tian, Yuxian Meng, Nanyun Peng, Fei Wu, Jiwei Li, Chun Fan
  • TLDR: We propose a new paradigm for paraphrase generation by treating the task as unsupervised machine translation (UMT) based on the assumption that there must be pairs of sentences expressing the same meaning in a large-scale unlabeled monolingual corpus.
• Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries
  • Xiaofei Sun, Zijun Sun, Yuxian Meng, Jiwei Li, Chun Fan
  • TLDR: We propose a novel method for generating coherent long texts that captures the high-level discourse dependencies between chunks of texts.
• CoCGAN: Contrastive Learning for Adversarial Category Text Generation
  • Xin Sheng, Linli Xu, Yinlong Xu, Changcun Bao, Huang Chen, Bo Ren
  • TLDR: We propose a novel contrastive adversarial category text generation model based on adversarial adversarial class relations and adversarial generative adversarial net.
• An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework Based on Semantic Blocks
  • Xinnian Liang, Jing Li, Shuangzhi Wu, Jiali Zeng, Yufan Jiang, Mu Li, Zhoujun Li
  • TLDR: We propose a novel and efficient framework for unsupervised long document summarization based on the semantic block.
• CHAE: Fine-Grained Controllable Story Generation with Characters, Actions and Emotions
  • Xinpeng Wang, Han Jiang, Zhihua Wei, Shanlin Zhou
  • TLDR: We propose a novel model for fine-grained control on the story generation, which allows the generation of customized stories with characters, corresponding actions and emotions arbitrarily assigned.
• Chinese Couplet Generation with Syntactic Information
  • Yan Song
  • TLDR: We propose to enhance Chinese couplet generation by leveraging syntactic information, i.e., part-of-speech tags and word dependencies.
• Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization
  • Yiming Wang, Qianren Mao, Junnan Liu, Weifeng Jiang, Hongdong Zhu, Jianxin Li
  • TLDR: We propose a novel entropy-constrained pseudo labeling strategy to obtain high-confidence labels from unlabeled predictions, which can obtain high confidence labels from unsupervised predictions by comparing the entropy of supervised and unsupervisioned predictions.
• Question Generation Based on Grammar Knowledge and Fine-grained Classification
  • Yuan Sun, Sisi Liu, Zhengcuo Dan, Xiaobing Zhao
  • TLDR: We propose a question type classifier and question generator for Tibetan and Hotpot QA.
• CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling
  • Yucheng Li, Chenghua Lin, Frank Guerin
  • TLDR: We present a novel multitask framework for Chinese Nominal Metaphor Generation, which improves the generation of novel metaphors with good readability and creativity.
• Psychology-guided Controllable Story Generation
  • Yuqiang Xie, Yue Hu, Yunpeng Li, Guanqun Bi, Luxi Xing, Wei Peng
  • TLDR: We propose a new story generation system that incorporates psychological state trackers and psychological state planners to generate controllable and well-planned stories.
• Few-shot Table-to-text Generation with Prefix-Controlled Generator
  • Yutao Luo, Menghua Lu, Gongshen Liu, Shilin Wang
  • TLDR: We propose a prompt-based approach, Prefix-Controlled Generator (i.e., PCG), for few-shot table-to-text generation.
• Text Simplification of College Admissions Instructions: A Professionally Simplified and Verified Corpus
  • Zachary W. Taylor, Maximus H. Chu, Junyi Jessy Li
  • TLDR: We present PSAT (Professionally Simplified Admissions Texts), a dataset with 112 admissions instructions randomly selected from higher education institutions across the US.
• On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART
  • Zebin Ou, Meishan Zhang, Yue Zhang
  • TLDR: We show that syntactic dependency knowledge in BART helps word ordering and empirically identify that syntact dependency knowledge is a reliable explanation.
• Visual Information Guided Zero-Shot Paraphrase Generation
  • Zhe Lin, Xiaojun Wan
  • TLDR: We propose visual information guided zero-shot paraphrase generation based only on paired image-caption data.
• Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning
  • Zhichao Geng, Ming Zhong, Zhangyue Yin, Xipeng Qiu, Xuanjing Huang
  • TLDR: We propose three speaker-aware supervised contrastive learning tasks for dialogue summarization, which can identify different speakers well in the dialogue.
• Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling
  • Zhixian Yang, Pengxuan Xu, Xiaojun Wan
  • TLDR: We propose a novel approach to generate text with linguistic annotation based on the posterior probabilities of part-of-speech.
• Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation
  • Zichen Wu, Xin Jia, Fanyi Qu, Yunfang Wu
  • TLDR: We propose answer localness modeling and syntactic mask attention to address the text structure of input passage in question generation task.
• LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases
  • Zichu Fei, Xin Zhou, Tao Gui, Qi Zhang, Xuanjing Huang
  • TLDR: We propose LFKQG, a controlled generation framework for Question Generation over Knowledge Bases, which improves the ability of existing KBQG models to adapt to the OOV predicates in real-world scenarios.
• Demystifying Neural Fake News via Linguistic Feature-Based Interpretation
  • Ankit Aich, Souvik Bhattacharya, Natalie Parde
  • TLDR: We show that stylistic features may be the most robust features of neural fake news generators.
• Measuring Geographic Performance Disparities of Offensive Language Classifiers
  • Brandon Lwowski, Paul Rad, Anthony Rios
  • TLDR: We present a comprehensive analysis of geographical-related content and their impact on performance disparities of offensive language detection models.
• Offensive Content Detection via Synthetic Code-Switched Text
  • Cesa Salaam, Franck Dernoncourt, Trung Bui, Danda Rawat, Seunghyun Yoon
  • TLDR: We present a synthetic code-switched textual dataset for online offensive content and a multi-lingual transformer-based classification model for online text.
• A Survey on Multimodal Disinformation Detection
  • Firoj Alam, Stefano Cresci, Tanmoy Chakraborty, Fabrizio Silvestri, Dimiter Dimitrov, Giovanni Da San Martino, Shaden Shaar, Hamed Firooz, Preslav Nakov
  • TLDR: We present a survey on the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, speech, video, social media network structure, and temporal information.
• Why Is It Hate Speech? Masked Rationale Prediction for Explainable Hate Speech Detection
  • Jiyun Kim, Byounghan Lee, Kyung-Ah Sohn
  • TLDR: We propose Masked Rationale Prediction as an intermediate task for hate speech detection.
• Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection
  • Tulika Bose, Nikolaos Aletras, Irina Illina, Dominique Fohr
  • TLDR: We propose a domain adaptation approach for hate-speech detection that learns to differentiate between domains and penalizes source-specific terms.
• Generalizable Implicit Hate Speech Detection Using Contrastive Learning
  • Youngwook Kim, Shinwoo Park, Yo-Sub Han
  • TLDR: We propose a novel contrastive learning method for detecting implicit hate speech in nuance and context.
• Social Bot-Aware Graph Neural Network for Early Rumor Detection
  • Zhen Huang, Zhilong Lv, Xiaoyun Han, Binyang Li, Menglong Lu, Dongsheng Li
  • TLDR: We present a novel graph neural network for early rumor detection and identify rumors within 3 hours.
• A Contrastive Cross-Channel Data Augmentation Framework for Aspect-Based Sentiment Analysis
  • Bing Wang, Liang Ding, Qihuang Zhong, Ximing Li, Dacheng Tao
  • TLDR: We propose a novel training framework for robustness of sentiment analysis based on in-domain contrastive cross-channel data augmentation and a filter for low-quality generated samples.
• Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis
  • Bowen Zhang, Xu Huang, Zhichao Huang, Hu Huang, Baoquan Zhang, Xianghua Fu, Liwen Jing
  • TLDR: We propose a novel neural network for aspect-term sentiment analysis that is interpretable and computational efficient.
• Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change Debate
  • Cedric Waterschoot, Ernst van den Hemel, Antal van den Bosch
  • TLDR: We propose a new method for filtering out rare and singular arguments in online climate change debate.
• A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction
  • Changzhi Zhou, Dandan Song, Jing Xu, Zhijing Wu
  • TLDR: We propose a document-level machine reading comprehension task for emotion cause analysis, which can model complicated relations between emotions and causes while avoiding generating the pairing matrix.
• Structural Bias for Aspect Sentiment Triplet Extraction
  • Chen Zhang, Lei Ren, Fang Ma, Jingang Wang, Wei Wu, Dawei Song
  • TLDR: We propose to address the efficiency issues of PLMs by using a cheap-to-compute relative position structure in place of the syntactic dependency structure.
• Unsupervised Data Augmentation for Aspect Based Sentiment Analysis
  • David Z. Chen, Adam Faulkner, Sahil Badyal
  • TLDR: We present an adaptation of Unsupervised Data Augmentation in semi-supervised learning for span-level span-based sentiment classification.
• A Sentiment and Emotion Aware Multimodal Multiparty Humor Recognition in Multilingual Conversational Setting
  • Dushyant Singh Chauhan, Gopendra Vikram Singh, Aseem Arora, Asif Ekbal, Pushpak Bhattacharyya
  • TLDR: We propose a multi-task framework for humor detection and emotion identification in multilingual language.
• TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
  • Duzhen Zhang, Zhen Yang, Fandong Meng, Xiuyi Chen, Jie Zhou
  • TLDR: We propose a novel two-stream attention model for causative emotion entailment in conversational utterance pair classification and show that it can outperforms existing models.
• Entity-Level Sentiment Analysis (ELSA): An Exploratory Task Survey
  • Egil Rønningstad, Erik Velldal, Lilja Øvrelid
  • TLDR: We present a suite of experiments aiming to assess the contribution towards ELSA provided by document-, sentence-, and target-level sentiment analysis, and provide a discussion of their shortcomings.
• Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification
  • Fei Zhao, Zhen Wu, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen
  • TLDR: We propose a novel Knowledge-enhanced Framework (KEF) in this paper, which can successfully exploit adjective-noun pairs extracted from the image to improve the visual attention capability and sentiment prediction capability of the TMSC task.
• Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline
  • Feng Ge, Weizhao Li, Haopeng Ren, Yi Cai
  • TLDR: We present a Chinese sticker-based multimodal dataset for the sentiment analysis task (CSMSA) and propose a new model for the task.
• Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora
  • Flor Miriam Plaza-del-Arco, María-Teresa Martín-Valdivia, Roman Klinger
  • TLDR: We analyze how sensitive a natural language inference-based zero-shot-learning classifier is to such changes to the prompt under consideration of the corpus: How carefully does the prompt need to be selected?
• CommunityLM: Probing Partisan Worldviews from Language Models
  • Hang Jiang, Doug Beeferman, Brandon Roy, Deb Roy
  • TLDR: We use community language models to query the worldviews of partisan Twitter users and show that they can be used to improve understanding of each other.
• Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification
  • Hao Niu, Yun Xiong, Jian Gao, Zhongchen Miao, Xiaosu Wang, Hongrun Ren, Yao Zhang, Yangyong Zhu
  • TLDR: We propose a novel Composition-based Heterogeneous Graph Multi-channel Attention Network to model inter-aspect relationships and aspect-context relationships simultaneously and propose a new Composition based Heterogeneity Graph Multi Channel Attention Network (CHGMAN) to encode the constructed heterogeneous graph.
• CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection
  • Jens Lemmens, Jens Van Nooten, Tim Kreutz, Walter Daelemans
  • TLDR: We present CoNTACT: a Dutch language model adapted to the domain of COVID-19 tweets.
• SSR: Utilizing Simplified Stance Reasoning Process for Robust Stance Detection
  • Jianhua Yuan, Yanyan Zhao, Yanyue Lu, Bing Qin
  • TLDR: We propose to incorporate the stance reasoning process as task knowledge to assist in learning genuine features and reducing reliance on bias features.
• Transferring Confluent Knowledge to Argument Mining
  • João António Rodrigues, António Branco
  • TLDR: We present a transfer learning methodology for argument mining that improves the performance of argument mining tasks and establishes new state of the art levels of performance for its three main sub-tasks.
• When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and Its Intensity
  • Khalid Alnajjar, Mika Hämäläinen, Jörg Tiedemann, Jorma Laaksonen, Mikko Kurimo
  • TLDR: We present an approach for automatically detecting humor in the Friends TV show using multimodal data.
• Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance
  • Longfeng Li, Haifeng Sun, Qi Qi, Jingyu Wang, Jing Wang, Jianxin Liao
  • TLDR: We propose a new method for aspect correlation modeling and selection of aspect feature in multiaspect sentiment analysis.
• Analyzing Persuasion Strategies of Debaters on Social Media
  • Matti Wiegmann, Khalid Al Khatib, Vishal Khanna, Benno Stein
  • TLDR: We propose to quantify debaters effectiveness in online discussion platform ChangeMyView in order to explore diverse insights into their persuasion strategies.
• KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features
  • Minghao Xu, Daling Wang, Shi Feng, Zhenfei Yang, Yifei Zhang
  • TLDR: We propose a novel Implicit Sentiment Analysis model combining Knowledge enhancement and Context features (dubbed KC-ISA) for text sentiment analysis.
• Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning
  • Qingyu Tan, Ruidan He, Lidong Bing, Hwee Tou Ng
  • TLDR: We propose a novel domain generalization method based on supervised contrastive learning with a memory-saving queue.
• A Zero-Shot Claim Detection Framework Using Question Answering
  • Revanth Gangi Reddy, Sai Chetan Chinthakindi, Yi R. Fung, Kevin Small, Heng Ji
  • TLDR: We propose a novel framework for fine-grained claim detection that leverages zero-shot Question Answering to solve a diverse set of sub-tasks such as topic filtering, claim object detection, and claimer detection.
• Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network
  • Rui Li, Cheng Liu, Dazhi Jiang
  • TLDR: We propose a novel asymmetric mutual learning strategy for multi-source unsupervised sentiment adaptation problem with the pre-trained features, which is more practical and challenging.
• Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection
  • Rui Liu, Zheng Lin, Huishan Ji, Jiangnan Li, Peng Fu, Weiping Wang
  • TLDR: We propose a novel target-aware semi-supervised few-shot stance detection framework that can achieve state-of-the-art performance on multiple benchmark datasets in the few-shots setting.
• Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction
  • Shunjie Chen, Xiaochuan Shi, Jingye Li, Shengqiong Wu, Hao Fei, Fei Li, Donghong Ji
  • TLDR: We propose two alignment mechanisms for emotion cause pair extraction that improve the performance of existing multi-task learning methods.
• Causal Intervention Improves Implicit Sentiment Analysis
  • Siyin Wang, Jie Zhou, Changzhi Sun, Junjie Ye, Tao Gui, Qi Zhang, Xuanjing Huang
  • TLDR: We propose a CausaL intervention model for implicit sentiment analysis using instrumental variable and show its great advantages over existing neural models.
• COMMA-DEER: COmmon-sense Aware Multimodal Multitask Approach for Detection of Emotion and Emotional Reasoning in Conversations
  • Soumitra Ghosh, Gopendra Vikram Singh, Asif Ekbal, Pushpak Bhattacharyya
  • TLDR: We present and discuss a novel task of detecting emotional reasoning (ER) and accompanying emotions in conversations.
• EmoMent: An Emotion Annotated Mental Health Corpus from Two South Asian Countries
  • Thushari Atapattu, Mahen Herath, Charitha Elvitigala, Piyanjali de Zoysa, Kasun Gunawardana, Menasha Thilakaratne, Kasun de Zoysa, Katrina Falkner
  • TLDR: We developed a novel emotion-annotated mental health corpus (EmoMent) extracted from two South Asian countries and used it to detect mental health issues from Facebook posts.
• LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis
  • Tianhao Gao, Jun Fang, Hanyu Liu, Zhiyuan Liu, Chao Liu, Pengzhang Liu, Yongjun Bao, Weipeng Yan
  • TLDR: We propose a unified generative multi-task framework for aspect-based sentiment analysis that can solve multiple ABSA tasks by controlling the type of task prompts consisting of multiple element prompts.
• A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis
  • Wei Chen, Jinglong Du, Zhao Zhang, Fuzhen Zhuang, Zhongshi He
  • TLDR: We propose a hierarchical interactive network for joint aspect-sentiment analysis, which can learn two-way interactions between two tasks appropriately, and it outperforms the existing methods on three real-world datasets.
• MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations
  • Weixiang Zhao, Yanyan Zhao, Bing Qin
  • TLDR: We propose Mutual Conversational Detachment Network (MuCDN) to clearly and effectively understand the conversational context by separating conversations into detached threads.
• UECA-Prompt: Universal Prompt for Emotion Cause Analysis
  • Xiaopeng Zheng, Zhiyue Liu, Zizhen Zhang, Zhaoyang Wang, Jiahai Wang
  • TLDR: We propose a universal prompt tuning method for emotion cause analysis and a cross-task training method for the same task.
• One-Teacher and Multiple-Student Knowledge Distillation on Sentiment Classification
  • Xiaoqin Chang, Sophia Yat Mei Lee, Suyang Zhu, Shoushan Li, Guodong Zhou
  • TLDR: We propose a novel one-teacher and multiple-student knowledge distillation approach to distill a deep pre-trained teacher model into multiple shallow student models with ensemble learning.
• Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks
  • Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
  • TLDR: Plugin-tuning improves the efficiency of existing parameter-efficient language models by re-using language modeling heads of pre-trained language models.
• A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction
  • Yichun Zhao, Kui Meng, Gongshen Liu, Jintao Du, Huijia Zhu
  • TLDR: We propose a Multi-Task Dual-Tree Network for extracting triplets from a given sentence with complex relations between aspects and opinions.
• Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction
  • Yidong Wang, Hao Wu, Ao Liu, Wenxin Hou, Zhen Wu, Jindong Wang, Takahiro Shinozaki, Manabu Okumura, Yue Zhang
  • TLDR: We propose a novel Multi-Grained Consistency Regularization method for TOWE that uses massive unlabeled data to reduce the risk of distribution shift between test data and training data.
• Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis
  • Yinglong Ma, Yunhe Pang
  • TLDR: We propose a learnable dependency-based double graph model for aspect-based sentiment classification.
• A Structure-Aware Argument Encoder for Literature Discourse Analysis
  • Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu
  • TLDR: We propose to separate tokens into two groups, namely framing tokens and topic tokens, to capture structural information of arguments.
• Mere Contrastive Learning for Cross-Domain Sentiment Analysis
  • Yun Luo, Fang Guo, Zihan Liu, Yue Zhang
  • TLDR: We propose a novel contrastive objective for cross-domain sentiment analysis and show that it can be used to improve the performance of existing cross-entropy-based methods.
• Exploiting Sentiment and Common Sense for Zero-shot Stance Detection
  • Yun Luo, Zihan Liu, Yuefeng Shi, Stan Z. Li, Yue Zhang
  • TLDR: We propose to boost the transferability of the stance detection model by using sentiment and commonsense knowledge, which are seldom considered in previous studies.
• Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis
  • Zijie Lin, Bin Liang, Yunfei Long, Yixue Dang, Min Yang, Min Zhang, Ruifeng Xu
  • TLDR: We propose a novel hierarchical graph contrastive learning framework for MSA, aiming to explore the intricate relations of intra- and inter-modal representations for sentiment extraction.
• AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis
  • Ziming Li, Yan Zhou, Weibo Zhang, Yaxin Liu, Chuanpeng Yang, Zheng Lian, Songlin Hu
  • TLDR: We propose a multimodal sentiment analysis model that learns to treat three modal features equally, without distinguishing the importance of different modalities; and a multimode-order-aware network that generalizes to more complex semantics.
• Keyphrase Prediction from Video Transcripts: New Dataset and Directions
  • Amir Pouran Ben Veyseh, Quan Hung Tran, Seunghyun Yoon, Varun Manjunatha, Hanieh Deilamsalehy, Rajiv Jain, Trung Bui, Walter W. Chang, Franck Dernoncourt, Thien Huu Nguyen
  • TLDR: We present a large-scale manually-annotated dataset for phrase prediction in video transcripts and show that the task is challenging in this domain.
• Event Extraction in Video Transcripts
  • Amir Pouran Ben Veyseh, Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen
  • TLDR: We propose a large-scale video transcript dataset for event extraction and show that it can be used to improve existing state-of-the-art event extraction systems.
• Recycle Your Wav2Vec2 Codebook: A Speech Perceiver for Keyword Spotting
  • Guillermo Cámbara, Jordi Luque, Mireia Farrús
  • TLDR: We show an efficient way of profiting from wav2vec2vec.0’s linguistic knowledge, by recycling the phonetic information encoded in its latent codebook, which has been typically thrown away after pretraining.
• Improving Code-switched ASR with Linguistic Information
  • Jie Chi, Peter Bell
  • TLDR: We propose to improve the performance of automatic speech recognition systems operating on code-switched speech by generating more realistic code-switchable text.
• Language-specific Effects on Automatic Speech Recognition Errors for World Englishes
  • June Choe, Yiran Chen, May Pik Yu Chan, Aini Li, Xin Gao, Nicole Holliday
  • TLDR: We investigate the performance of Otter’s automatic captioning system on native and non-native English speakers of different language background through a linguistic analysis of segment-level errors.
• A Transformer-based Threshold-Free Framework for Multi-Intent NLU
  • Lisung Chen, Nuo Chen, Yuexian Zou, Yong Wang, Xinzhong Sun
  • TLDR: We propose a transformer-based Threshold-Free Multi-intent NLU model with multi-task learning and a threshold-free intent multi-intent classifier.
• Unsupervised Multi-scale Expressive Speaking Style Modeling with Hierarchical Context Information for Audiobook Speech Synthesis
  • Xueyuan Chen, Shun Lei, Zhiyong Wu, Dong Xu, Weifeng Zhao, Helen Meng
  • TLDR: We propose an unsupervised multi-scale context-sensitive text-to-speech model for audiobooks.
• Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
  • Yangjun Wu, Han Wang, Dongxiang Zhang, Gang Chen, Hao Zhang
  • TLDR: We propose a unified generative framework for multiple intent detection and slot filling that learns to answer question-answering questions.
• Adaptive Unsupervised Self-training for Disfluency Detection
  • Zhongyuan Wang, Yixuan Wang, Shaolei Wang, Wanxiang Che
  • TLDR: We propose an adaptive unsupervised self-training method for disfluency detection, which improves 2.3 points over the current SOTA unsupervision.