🔥 热搜 🔥
百度今日热点微信公众平台贴吧opggdnf私服百度贴吧知乎dnf公益服百度傻逼
分类
社会娱乐国际人权科技经济其它
🔥 热搜 🔥
上海习近平新疆鄂州父女瓜乌鲁木齐疫情H工口小学生赛高习明泽芊川一笑图包印尼排华
分类
社会娱乐国际人权科技经济其它
查看原文
其他

EMNLP2021主会议191篇短文、Findings305篇长文及119篇短文分类-附论文链接

刘聪NLP NLP工作站 2023-11-28
收录于合集 #论文集合 19个
大家好，我是刘聪NLP。
前两天，将EMNLP2021 主会议的656篇长文进行了分类（EMNLP2021主会议-656篇长文分类-附论文链接），周末笔者又将主会议191篇短文、Findings305篇长文及119篇短文分类进行了分类。
由于长文分享时，官方链接还未放出，因此缺失部分链接，现已在知乎补全，系列文章如下：
https://zhuanlan.zhihu.com/p/429144912
https://zhuanlan.zhihu.com/p/429161700
https://zhuanlan.zhihu.com/p/430197671
https://zhuanlan.zhihu.com/p/430517383
https://zhuanlan.zhihu.com/p/430531571
归类划分（论文附带链接），主要包括：45篇QA系统（阅读理解、问答、检索）、19篇情感分析（方面级情感分析、篇章集情感分析、情绪分析）、30篇对话系统、20篇信息抽取（关键词抽取、术语抽取、实体抽取、实体分类、关系抽取、事件抽取、观点抽取）、4篇事件检测、57篇预训练语言模型应用（Transformer优化、语言模型下游应用、语言模型探索、分析等）、35篇数据集、任务及评估、31篇机器翻译、57篇多模态、27篇摘要（对话摘要、多文档摘要、代码摘要）、40篇文本生成（段落生成、对话生成、复述、问题生成）、5篇文本风格改写、7篇推理（因果推断、多跳推理、知识推理、常识推理）、17篇模型鲁棒性及对抗、4篇模型压缩（模型优化、剪枝、蒸馏）、16篇小样本（元学习、零样本、低资源）、21篇知识表征、14篇多语言、26篇社会道德伦理偏见、1篇虚假新闻检测、10篇指代、链指、消歧及对齐、6篇数据增强、6篇纠错、18篇图相关、14篇文本分类、19篇NLP基础（分词、词性、语义理解、句法分析）、66篇其他。
注意：有部分论文可能包含多个类别的内容，笔者仅凭自己理解进行了单分类。
文章篇幅较长、各位同学可以自行找到自己感兴趣的分类。赶紧去看论文吧！！！
整理不易，请多多关注、转发、点赞。也请多多关注本人知乎「刘聪NLP」，有问题的朋友也欢迎加我微信私聊。

QA系统

主会议-Short
1、TowardDeconfounding the Influence of Entity Demographics for Question AnsweringAccuracy
https://aclanthology.org/2021.emnlp-main.444/
2、What’s in a Name?Answer Equivalence For Open-Domain Question Answering
https://aclanthology.org/2021.emnlp-main.757/
3、GenerativeContext Pair Selection for Multi-hop Question Answering
https://aclanthology.org/2021.emnlp-main.561/
4、Context-AwareInteraction Network for Question Matching
https://aclanthology.org/2021.emnlp-main.312/
5、Have You SeenThat Number? Investigating Extrapolation in Question Answering Models
https://aclanthology.org/2021.emnlp-main.563/
6、ExpandingEnd-to-End Question Answering on Differentiable Knowledge Graphs withIntersection
https://aclanthology.org/2021.emnlp-main.694/
7、Dealing withTypos for BERT-based Passage Retrieval and Ranking
https://aclanthology.org/2021.emnlp-main.225/
8、End-to-End EntityResolution and Question Answering Using Differentiable Knowledge Graphs
https://aclanthology.org/2021.emnlp-main.345/
9、Locke's Holiday:Belief Bias in Machine Reading
https://aclanthology.org/2021.emnlp-main.649/
10、EvaluationParadigms in Question Answering
https://aclanthology.org/2021.emnlp-main.758/
11、Simple andEffective Unsupervised Redundancy Elimination to Compress Dense Vectors forPassage Retrieval
https://aclanthology.org/2021.emnlp-main.227/
12、Perhaps PTLMsShould Go to School – A Task to Assess Open Book and Closed Book QA
https://aclanthology.org/2021.emnlp-main.493/
13、Multi-VectorAttention Models for Deep Re-ranking
https://aclanthology.org/2021.emnlp-main.443/
14、Single-datasetExperts for Multi-dataset Question Answering
https://aclanthology.org/2021.emnlp-main.495/
15、SimpleEntity-Centric Questions Challenge Dense Retrievers
https://aclanthology.org/2021.emnlp-main.496/
16、Numericalreasoning in machine reading comprehension tasks: are we there yet?
https://aclanthology.org/2021.emnlp-main.759/
Findings-Long
1、ImprovingEmbedding-based Large-scale Retrieval via Label Enhancement
https://aclanthology.org/2021.findings-emnlp.13/
2、DecomposingComplex Questions Makes Multi-Hop QA Easier and More Interpretable
https://aclanthology.org/2021.findings-emnlp.17/
3、DenseHierarchical Retrieval for Open-domain Question Answering
https://aclanthology.org/2021.findings-emnlp.19/
4、Multi-Task DenseRetrieval via Model Uncertainty Fusion for Open-Domain Question Answering
https://aclanthology.org/2021.findings-emnlp.26/
5、GooAQ: OpenQuestion Answering with Diverse Answer Types
https://aclanthology.org/2021.findings-emnlp.38/
6、Recall and Learn:A Memory-augmented Solver for Math Word Problems
https://aclanthology.org/2021.findings-emnlp.68/
7、R2-D2: A ModularBaseline for Open-Domain Question Answering
https://aclanthology.org/2021.findings-emnlp.73/
8、WhenRetriever-Reader Meets Scenario-Based Multiple-Choice Questions
https://aclanthology.org/2021.findings-emnlp.84/
9、Less Is More:Domain Adaptation with Lottery Ticket for Reading Comprehension
https://aclanthology.org/2021.findings-emnlp.95/
10、ExploitingReasoning Chains for Multi-hop Science Question Answering
https://aclanthology.org/2021.findings-emnlp.99/
11、Grounded GraphDecoding improves Compositional Generalization in Question Answering
https://aclanthology.org/2021.findings-emnlp.157/
12、A PretrainingNumerical Reasoning Model for Ordinal Constrained Question Answering onKnowledge Base
https://aclanthology.org/2021.findings-emnlp.159/
13、RoR:Read-over-Read for Long Document Machine Reading Comprehension
https://aclanthology.org/2021.findings-emnlp.160/
14、EnhancingDual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval
https://aclanthology.org/2021.findings-emnlp.198/
15、What IfSentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension
https://aclanthology.org/2021.findings-emnlp.202/
16、CodeQA: AQuestion Answering Dataset for Source Code Comprehension
https://aclanthology.org/2021.findings-emnlp.223/
17、Extract,Integrate, Compete: Towards Verification Style Reading Comprehension
https://aclanthology.org/2021.findings-emnlp.255/
18、Knowledge-EnhancedEvidence Retrieval for Counterargument Generation
https://aclanthology.org/2021.findings-emnlp.264/
19、Relation-GuidedPre-Training for Open-Domain Question Answering
https://aclanthology.org/2021.findings-emnlp.292/
20、Can NLI ModelsVerify QA Systems' Predictions?
https://aclanthology.org/2021.findings-emnlp.324/
21、QuestionAnswering over Electronic Devices: A New Benchmark Dataset and a Multi-TaskLearning based QA Framework
https://aclanthology.org/2021.findings-emnlp.392/
22、ADivide-And-Conquer Approach for Multi-label Multi-hop Relation Detection inKnowledge Base Question Answering
https://aclanthology.org/2021.findings-emnlp.412/
Findings-Short
1、Unseen EntityHandling in Complex Question Answering over Knowledge Base via LanguageGeneration
https://aclanthology.org/2021.findings-emnlp.50/
2、RobustFragment-Based Framework for Cross-lingual Sentence Retrieval
https://aclanthology.org/2021.findings-emnlp.80/
3、WinnowingKnowledge for Multi-choice Question Answering
https://aclanthology.org/2021.findings-emnlp.100/
4、ImprovingNumerical Reasoning Skills in the Modular Approach for Complex QuestionAnswering on Text
https://aclanthology.org/2021.findings-emnlp.231/
5、Reference-basedWeak Supervision for Answer Sentence Selection using Web Data
https://aclanthology.org/2021.findings-emnlp.363/
6、Do We Know WhatWe Don’t Know? Studying Unanswerable Questions beyond SQuAD 2.0
https://aclanthology.org/2021.findings-emnlp.385/
7、AutoEQA:Auto-Encoding Questions for Extractive Question Answering
https://aclanthology.org/2021.findings-emnlp.403/

情感分析

主会议-short
1、Semantics-PreservedData Augmentation for Aspect-Based Sentiment Analysis
https://aclanthology.org/2021.emnlp-main.362/
2、BERT4GCN: UsingBERT Intermediate Layers to Augment GCN for Aspect-based SentimentClassification
https://aclanthology.org/2021.emnlp-main.724/
3、The Effect ofRound-Trip Translation on Fairness in Sentiment Analysis
https://aclanthology.org/2021.emnlp-main.363/
4、Open AspectTarget Sentiment Classification with Natural Language Prompts
https://aclanthology.org/2021.emnlp-main.509/
5、NB-MLM: EfficientDomain Adaptation of Masked Language Models for Sentiment Analysis
https://aclanthology.org/2021.emnlp-main.717/
6、Guilt byAssociation: Emotion Intensities in Lexical Representations
https://aclanthology.org/2021.emnlp-main.781/
7、Emotion Inferencein Multi-Turn Conversations with Addressee-Aware Module and Ensemble Strategy
https://aclanthology.org/2021.emnlp-main.320/
Findings-Long
1、BidirectionalHierarchical Attention Networks based on Document-level Context for Emotion CauseExtraction
https://aclanthology.org/2021.findings-emnlp.51/
2、Recommend for aReason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction
https://aclanthology.org/2021.findings-emnlp.66/
3、Past, Present,and Future: Conversational Emotion Recognition through Structural Modeling ofPsychological Knowledge
https://aclanthology.org/2021.findings-emnlp.104/
4、SelfQuestion-answering: Aspect-based Sentiment Analysis by Role Flipped MachineReading Comprehension
https://aclanthology.org/2021.findings-emnlp.115/
5、An IterativeMulti-Knowledge Transfer Network for Aspect-Based Sentiment Analysis
https://aclanthology.org/2021.findings-emnlp.152/
6、Uncovering theLimits of Text-based Emotion Detection
https://aclanthology.org/2021.findings-emnlp.219/
7、Knowledge-InteractiveNetwork with Sentiment Polarity Intensity-Aware Multi-Task Learning for EmotionRecognition in Conversations
https://aclanthology.org/2021.findings-emnlp.245/
8、A Discourse-AwareGraph Neural Network for Emotion Recognition in Multi-Party Conversation
https://aclanthology.org/2021.findings-emnlp.252/
9、EliminatingSentiment Bias for Aspect-Level Sentiment Classification with UnsupervisedOpinion Extraction
https://aclanthology.org/2021.findings-emnlp.258/
10、DistillingKnowledge for Empathy Detection
https://aclanthology.org/2021.findings-emnlp.314/
11、Aspect-basedSentiment Analysis in Question Answering Forums
https://aclanthology.org/2021.findings-emnlp.390/
Findings-Short
1、Casting the SameSentiment Classification Problem
https://aclanthology.org/2021.findings-emnlp.53/

对话系统

主会议-Short
1、Generation andExtraction Combined Dialogue State Tracking with Hierarchical OntologyIntegration
https://aclanthology.org/2021.emnlp-main.171/
2、Looking forConfirmations: An Effective and Human-Like Visual Dialogue Strategy
https://aclanthology.org/2021.emnlp-main.736/
3、Is InformationDensity Uniform in Task-Oriented Dialogues?
https://aclanthology.org/2021.emnlp-main.652/
4、We've had thisconversation before: A Novel Approach to Measuring Dialog Similarity
https://aclanthology.org/2021.emnlp-main.89/
5、CSAGN:Conversational Structure Aware Graph Network for Conversational Semantic RoleLabeling
https://aclanthology.org/2021.emnlp-main.177/
6、Zero-ShotDialogue Disentanglement by Self-Supervised Entangled Response Selection
https://aclanthology.org/2021.emnlp-main.400/
7、A CollaborativeMulti-agent Reinforcement Learning Framework for Dialog Action Decomposition
https://aclanthology.org/2021.emnlp-main.621/
8、DynamicForecasting of Conversation Derailment
https://aclanthology.org/2021.emnlp-main.624/
9、ContextualRephrase Detection for Reducing Friction in Dialogue Systems
https://aclanthology.org/2021.emnlp-main.143/
10、InvestigatingRobustness of Dialog Models to Popular Figurative Language Constructs
https://aclanthology.org/2021.emnlp-main.592/
11、"It doesn'tlook good for a date": Transforming Critiques into Preferences for ConversationalRecommendation Systems
https://aclanthology.org/2021.emnlp-main.145/
12、EffectiveSequence-to-Sequence Dialogue State Tracking
https://aclanthology.org/2021.emnlp-main.593/
Findings-Long
1、A Model ofCross-Lingual Knowledge-Grounded Response Generation for Open-Domain DialogueSystems
https://aclanthology.org/2021.findings-emnlp.33/
2、ImprovingEmpathetic Response Generation by Recognizing Emotion Cause in Conversations
https://aclanthology.org/2021.findings-emnlp.70/
3、KERS: AKnowledge-Enhanced Framework for Recommendation Dialog Systems with MultipleSubgoals
https://aclanthology.org/2021.findings-emnlp.94/
4、ImprovingDialogue State Tracking with Turn-based Loss Function and Sequential DataAugmentation
https://aclanthology.org/2021.findings-emnlp.144/
5、Self- andPseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-partyDialogue Reading Comprehension
https://aclanthology.org/2021.findings-emnlp.176/
6、Re-entryPrediction for Online Conversations via Self-Supervised Learning
https://aclanthology.org/2021.findings-emnlp.183/
7、ConstructingEmotional Consensus and Utilizing Unpaired Data for Empathetic DialogueGeneration
https://aclanthology.org/2021.findings-emnlp.268/
8、Distilling theKnowledge of Large-scale Generative Models into Retrieval Models for EfficientOpen-domain Conversation
https://aclanthology.org/2021.findings-emnlp.286/
9、DetectingCommunity Sensitive Norm Violations in Online Conversations
https://aclanthology.org/2021.findings-emnlp.288/
10、Refine andImitate: Reducing Repetition and Inconsistency in Persuasion Dialogues viaReinforcement Learning and Human Demonstration
https://aclanthology.org/2021.findings-emnlp.295/
11、Retrieval AugmentationReduces Hallucination in Conversation
https://aclanthology.org/2021.findings-emnlp.320/
12、ProbingCommonsense Explanation in Dialogue Response Generation
https://aclanthology.org/2021.findings-emnlp.349/
13、FCM: AFine-grained Comparison Model for Multi-turn Dialogue Reasoning
https://aclanthology.org/2021.findings-emnlp.362/
14、Task-OrientedClustering for Dialogues
https://aclanthology.org/2021.findings-emnlp.368/
Findings-Short
1、ImprovingEnd-to-End Task-Oriented Dialog System with A Simple Auxiliary Task
https://aclanthology.org/2021.findings-emnlp.112/
2、TIAGE: ABenchmark for Topic-Shift Aware Dialog Modeling
https://aclanthology.org/2021.findings-emnlp.145/
3、Speaker TurnModeling for Dialogue Act Classification
https://aclanthology.org/2021.findings-emnlp.185/
4、SpeculativeSampling in Variational Autoencoders for Dialogue Response Generation
https://aclanthology.org/2021.findings-emnlp.407/
 

信息抽取

主会议-Short
1、An EmpiricalStudy on Leveraging Position Embeddings for Target-oriented Opinion WordsExtraction
https://aclanthology.org/2021.emnlp-main.722/
2、RockNER: A SimpleMethod to Create Adversarial Examples for Evaluating the Robustness of NamedEntity Recognition Models
https://aclanthology.org/2021.emnlp-main.302/
3、UnsupervisedParaphrasing Consistency Training for Low Resource Named Entity Recognition
https://aclanthology.org/2021.emnlp-main.430/
4、XLEnt: Mining aLarge Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic WordAlignment
https://aclanthology.org/2021.emnlp-main.814/
5、Towards RealisticFew-Shot Relation Extraction
https://aclanthology.org/2021.emnlp-main.433/
6、UtilizingRelative Event Time to Enhance Event-Event Temporal Relation Extraction
https://aclanthology.org/2021.emnlp-main.815/
7、RelationExtraction with Word Graphs from N-grams
https://aclanthology.org/2021.emnlp-main.228/
8、SeparatingRetention from Extraction in the Evaluation of End-to-end Relation Extraction
https://aclanthology.org/2021.emnlp-main.816/
Findings-Long
1、HiTRANS: AHierarchical Transformer Network for Nested Named Entity Recognition
https://aclanthology.org/2021.findings-emnlp.12/
2、ExploringSentence Community for Document-Level Event Extraction
https://aclanthology.org/2021.findings-emnlp.32/
3、Semi-supervisedRelation Extraction via Incremental Meta Self-Training
https://aclanthology.org/2021.findings-emnlp.44/
4、DistantlySupervised Relation Extraction in Federated Settings
https://aclanthology.org/2021.findings-emnlp.52/
5、ImprovingDistantly-Supervised Named Entity Recognition with Self-Collaborative DenoisingLearning
https://aclanthology.org/2021.findings-emnlp.131/
6、Learning fromLanguage Description: Low-shot Named Entity Recognition via DecomposedFramework
https://aclanthology.org/2021.findings-emnlp.139/
7、REBEL: RelationExtraction By End-to-end Language generation
https://aclanthology.org/2021.findings-emnlp.204/
8、WikiNEuRal:Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER
https://aclanthology.org/2021.findings-emnlp.215/
Findings-Short
1、A Web ScaleEntity Extraction System
https://aclanthology.org/2021.findings-emnlp.7/
2、CNNBiF: CNN-basedBigram Features for Named Entity Recognition
https://aclanthology.org/2021.findings-emnlp.87/
3、GenerativeRE:Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity andRelation Extraction
https://aclanthology.org/2021.findings-emnlp.182/
4、Towards RealisticSingle-Task Continuous Learning Research for NER
https://aclanthology.org/2021.findings-emnlp.319/
 

事件检测

主会议-Short
1、Honey or Poison?Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention
https://aclanthology.org/2021.emnlp-main.637/
2、LearningPrototype Representations Across Few-Shot Tasks for Event Detection
https://aclanthology.org/2021.emnlp-main.427/
Findings-Long
1、Self-AttentionGraph Residual Convolutional Networks for Event Detection with dependencyrelations
https://aclanthology.org/2021.findings-emnlp.28/
2、IncorporatingCircumstances into Narrative Event Prediction
https://aclanthology.org/2021.findings-emnlp.416/

预训练语言模型应用

主会议-Short
1、Improving andSimplifying Pattern Exploiting Training
https://aclanthology.org/2021.emnlp-main.407/
2、Explore BetterRelative Position Embeddings from Encoding Perspective for Transformer Models
https://aclanthology.org/2021.emnlp-main.237/
3、AdapterDrop: Onthe Efficiency of Adapters in Transformers
https://aclanthology.org/2021.emnlp-main.626/
4、Enjoy theSalience: Towards Better Transformer-based Faithful Explanations with WordSalience
https://aclanthology.org/2021.emnlp-main.645/
5、Mixture-of-Partitions:Infusing Large Biomedical Knowledge Graphs into BERT
https://aclanthology.org/2021.emnlp-main.383/
6、BERT-Beta: AProactive Probabilistic Approach to Text Moderation
https://aclanthology.org/2021.emnlp-main.682/
7、ExploringUnderexplored Limitations of Cross-Domain Text-to-SQL Generalization
https://aclanthology.org/2021.emnlp-main.702/
8、BPM_MT: EnhancedBackchannel Prediction Model using Multi-Task Learning
https://aclanthology.org/2021.emnlp-main.277/
9、SHAPE: ShiftedAbsolute Position Embedding for Transformers
https://aclanthology.org/2021.emnlp-main.266/
10、FrustratinglySimple Pretraining Alternatives to Masked Language Modeling
https://aclanthology.org/2021.emnlp-main.249/
11、AVocaDo:Strategy for Adapting Vocabulary to Downstream Domain
https://aclanthology.org/2021.emnlp-main.385/
12、How to TrainBERT with an Academic Budget
https://aclanthology.org/2021.emnlp-main.831/
13、Sequence Lengthis a Domain: Length-based Overfitting in Transformer Models
https://aclanthology.org/2021.emnlp-main.650/
14、Beyond PreservedAccuracy: Evaluating Loyalty and Robustness of BERT Compression
https://aclanthology.org/2021.emnlp-main.832/
15、PICARD: ParsingIncrementally for Constrained Auto-Regressive Decoding from Language Models
https://aclanthology.org/2021.emnlp-main.779/
16、SentenceBottleneck Autoencoders from Transformer Language Models
https://aclanthology.org/2021.emnlp-main.137/
17、IndoBERTweet: APretrained Language Model for Indonesian Twitter with Effective Domain-SpecificVocabulary Initialization
https://aclanthology.org/2021.emnlp-main.833/
18、A SimpleGeometric Method for Cross-Lingual Linguistic Transformations with Pre-trainedAutoencoders
https://aclanthology.org/2021.emnlp-main.792/
19、BoostingCross-Lingual Transfer via Self-Learning with Uncertainty Estimation
https://aclanthology.org/2021.emnlp-main.538/
20、Numeracyenhances the Literacy of Language Models
https://aclanthology.org/2021.emnlp-main.557/
21、Can LanguageModels be Biomedical Knowledge Bases?
https://aclanthology.org/2021.emnlp-main.388/
22、ClauseRec: AClause Recommendation Framework for AI-aided Contract Authoring
https://aclanthology.org/2021.emnlp-main.691/
23、A ThoroughEvaluation of Task-Specific Pretraining for Summarization
https://aclanthology.org/2021.emnlp-main.12/
Findings-Long
1、K-PLUG:Knowledge-injected Pre-trained Language Model for Natural LanguageUnderstanding and Generation in E-Commerce
https://aclanthology.org/2021.findings-emnlp.1/
2、CascadeBERT:Accelerating Inference of Pre-trained Language Models via Calibrated CompleteModels Cascade
https://aclanthology.org/2021.findings-emnlp.43/
3、Distilling WordMeaning in Context from Pre-trained Language Models
https://aclanthology.org/2021.findings-emnlp.49/
4、Probing AcrossTime: What Does RoBERTa Know and When?
https://aclanthology.org/2021.findings-emnlp.71/
5、Knowledge-GuidedParaphrase Identification
https://aclanthology.org/2021.findings-emnlp.72/
6、Task-adaptivePre-training and Self-training are Complementary for Natural LanguageUnderstanding
https://aclanthology.org/2021.findings-emnlp.86/
7、Span Fine-tuningfor Pre-trained Language Models
https://aclanthology.org/2021.findings-emnlp.169/
8、GiBERT: EnhancingBERT with Linguistic Information using a Lightweight Gated Injection Method
https://aclanthology.org/2021.findings-emnlp.200/
9、Wine is not v in. On the Compatibility of Tokenizations across Languages
https://aclanthology.org/2021.findings-emnlp.205/
10、Skim-Attention:Learning to Focus via Document Layout
https://aclanthology.org/2021.findings-emnlp.207/
11、Challenges inDetoxifying Language Models
https://aclanthology.org/2021.findings-emnlp.210/
12、CDLM:Cross-Document Language Modeling
https://aclanthology.org/2021.findings-emnlp.225/
13、ControlledNeural Sentence-Level Reframing of News Articles
https://aclanthology.org/2021.findings-emnlp.228/
14、NewsBERT:Distilling Pre-trained Language Model for Intelligent News Application
https://aclanthology.org/2021.findings-emnlp.280/
15、Exploring aUnified Sequence-To-Sequence Transformer for Medical Product Safety Monitoringin Social Media
https://aclanthology.org/2021.findings-emnlp.300/
16、Parameter-EfficientDomain Knowledge Integration from Multiple Sources for Biomedical Pre-trainedLanguage Models
https://aclanthology.org/2021.findings-emnlp.325/
17、Want To ReduceLabeling Cost? GPT-3 Can Help
https://aclanthology.org/2021.findings-emnlp.354/
Findings-Short
1、EuphemisticPhrase Detection by Masked Language Model
https://aclanthology.org/2021.findings-emnlp.16/
2、Rethinking WhyIntermediate-Task Fine-Tuning Works
https://aclanthology.org/2021.findings-emnlp.61/
3、Learning HardRetrieval Decoder Attention for Transformers
https://aclanthology.org/2021.findings-emnlp.67/
4、ArabicTransformer:Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective
https://aclanthology.org/2021.findings-emnlp.108/
5、On the Effects ofTransformer Size on In- and Out-of-Domain Calibration
https://aclanthology.org/2021.findings-emnlp.180/
6、Is BERT aCross-Disciplinary Knowledge Learner? A Surprising Finding of Pre-trainedModels' Transferability
https://aclanthology.org/2021.findings-emnlp.189/
7、Attention-basedContrastive Learning for Winograd Schemas
https://aclanthology.org/2021.findings-emnlp.208/
8、ProbingPre-trained Language Models for Semantic Attributes and their Values
https://aclanthology.org/2021.findings-emnlp.218/
9、MeLT:Message-Level Transformer with Masked Document Representations as Pre-Trainingfor Stance Detection
https://aclanthology.org/2021.findings-emnlp.253/
10、LMSOC: AnApproach for Socially Sensitive Pretraining
https://aclanthology.org/2021.findings-emnlp.254/
11、How DoesFine-tuning Affect the Geometry of Embedding Space: A Case Study on Isotropy
https://aclanthology.org/2021.findings-emnlp.261/
12、InvestigatingNumeracy Learning Ability of a Text-to-Text Transfer Model
https://aclanthology.org/2021.findings-emnlp.265/
13、Transformer overPre-trained Transformer for Neural Text Segmentation with Enhanced TopicCoherence
https://aclanthology.org/2021.findings-emnlp.283/
14、LAMAD: A LinguisticAttentional Model for Arabic Text Diacritization
https://aclanthology.org/2021.findings-emnlp.317/
15、Subformer:Exploring Weight Sharing for Parameter Efficiency in Generative Transformers
https://aclanthology.org/2021.findings-emnlp.344/
16、Reconsideringthe Past: Optimizing Hidden States in Language Models
https://aclanthology.org/2021.findings-emnlp.346/
17、Bag of Tricksfor Optimizing Transformer Efficiency
https://aclanthology.org/2021.findings-emnlp.357/

数据集、任务及评估

主会议-Short
1、Cryptonite: ACryptic Crossword Benchmark for Extreme Ambiguity in Language
https://aclanthology.org/2021.emnlp-main.344/
2、Data-QuestEval: AReferenceless Metric for Data-to-Text Semantic Evaluation
https://aclanthology.org/2021.emnlp-main.633/
3、An EvaluationDataset and Strategy for Building Robust Multi-turn Response Selection Model
https://aclanthology.org/2021.emnlp-main.180/
4、CHoRaL:Collecting Humor Reaction Labels from Millions of Social Media Users
https://aclanthology.org/2021.emnlp-main.364/
5、ExtractingFine-Grained Knowledge Graphs of Scientific Claims: Dataset andTransformer-Based Results
https://aclanthology.org/2021.emnlp-main.381/
6、COUGH: AChallenge Dataset and Models for COVID-19 FAQ Retrieval
https://aclanthology.org/2021.emnlp-main.305/
7、Chinese WPLC: AChinese Dataset for Evaluating Pretrained Language Models on Word PredictionGiven Long-Range Context
https://aclanthology.org/2021.emnlp-main.306/
Findings-Long
1、Self-TeachingMachines to Read and Comprehend with Large-Scale Multi-SubjectQuestion-Answering Data
https://aclanthology.org/2021.findings-emnlp.6/
2、TWEETSUMM - ADialog Summarization Dataset for Customer Service
https://aclanthology.org/2021.findings-emnlp.24/
3、Structuredabbreviation expansion in context
https://aclanthology.org/2021.findings-emnlp.85/
4、Neural Media BiasDetection Using Distant Supervision With BABE - Bias Annotations By Experts
https://aclanthology.org/2021.findings-emnlp.101/
5、"Let YourCharacters Tell Their Story": A Dataset for Character-Centric NarrativeUnderstanding
https://aclanthology.org/2021.findings-emnlp.150/
6、WIKIBIAS:Detecting Multi-Span Subjective Biases in Language
https://aclanthology.org/2021.findings-emnlp.155/
7、ContractNLI: ADataset for Document-level Natural Language Inference for Contracts
https://aclanthology.org/2021.findings-emnlp.164/
8、Retrieve,Discriminate and Rewrite: A Simple and Effective Framework for ObtainingAffective Response in Retrieval-Based Chatbots
https://aclanthology.org/2021.findings-emnlp.168/
9、DIRECT: Directand Indirect Responses in Conversational Text Corpus
https://aclanthology.org/2021.findings-emnlp.170/
10、UniteD-SRL: AUnified Dataset for Span- and Dependency-Based Multilingual and Cross-LingualSemantic Role Labeling
https://aclanthology.org/2021.findings-emnlp.197/
11、Collecting aLarge-Scale Gender Bias Dataset for Coreference Resolution and MachineTranslation
https://aclanthology.org/2021.findings-emnlp.211/
12、Patterns ofPolysemy and Homonymy in Contextualised Language Models
https://aclanthology.org/2021.findings-emnlp.226/
13、AutomaticDiscrimination between Inherited and Borrowed Latin Words in Romance Languages
https://aclanthology.org/2021.findings-emnlp.243/
14、Monolingual andCross-Lingual Acceptability Judgments with the Italian CoLA corpus
https://aclanthology.org/2021.findings-emnlp.250/
15、AStitchInLanguageModels:Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained LanguageModels
https://aclanthology.org/2021.findings-emnlp.294/
16、Evidence-basedFact-Checking of Health-related Claims
https://aclanthology.org/2021.findings-emnlp.297/
17、AutomaticBilingual Markup Transfer
https://aclanthology.org/2021.findings-emnlp.299/
18、NOAHQA: NumericalReasoning with Interpretable Graph Question Answering Dataset
https://aclanthology.org/2021.findings-emnlp.350/
19、DiscoveringExplanatory Sentences in Legal Case Decisions Using Pre-trained Language Models
https://aclanthology.org/2021.findings-emnlp.361/
20、'Just What doYou Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP
https://aclanthology.org/2021.findings-emnlp.414/
21、Does Putting aLinguist in the Loop Improve NLU Data Collection
https://aclanthology.org/2021.findings-emnlp.421/
22、Tiered Reasoningfor Intuitive Physics: Toward Verifiable Commonsense Language Understanding
https://aclanthology.org/2021.findings-emnlp.422/
Findings-Short
1、EDTC: A Corpusfor Discourse-Level Topic Chain Parsing
https://aclanthology.org/2021.findings-emnlp.113/
2、SentNoB: ADataset for Analysing Sentiment on Noisy Bangla Texts
https://aclanthology.org/2021.findings-emnlp.278/
3、Adapting Entitiesacross Languages and Cultures
https://aclanthology.org/2021.findings-emnlp.315/
4、From None toSevere: Predicting Severity in Movie Scripts
https://aclanthology.org/2021.findings-emnlp.332/
5、NUANCED: NaturalUtterance Annotation for Nuanced Conversation with Estimated Distributions
https://aclanthology.org/2021.findings-emnlp.337/
6、ForumSum: AMulti-Speaker Conversation Summarization Dataset
https://aclanthology.org/2021.findings-emnlp.391/

机器翻译

主会议-Short
1、Neural MachineTranslation with Heterogeneous Topic Knowledge Embeddings
https://aclanthology.org/2021.emnlp-main.256/
2、Improving NeuralMachine Translation by Bidirectional Training
https://aclanthology.org/2021.emnlp-main.263/
3、What’s Hidden ina One-layer Randomly Weighted Transformer?
https://aclanthology.org/2021.emnlp-main.231/
4、An EmpiricalInvestigation of Word Alignment Supervision for Zero-Shot Multilingual NeuralMachine Translation
https://aclanthology.org/2021.emnlp-main.664/
5、Improving theQuality Trade-Off for Neural Machine Translation Multi-Domain Adaptation
https://aclanthology.org/2021.emnlp-main.666/
6、PhoMT: AHigh-Quality and Large-Scale Benchmark Dataset for Vietnamese-English MachineTranslation
https://aclanthology.org/2021.emnlp-main.369/
7、ImprovingSimultaneous Translation by Incorporating Pseudo-References with FewerReorderings
https://aclanthology.org/2021.emnlp-main.473/
8、Data andParameter Scaling Laws for Neural Machine Translation
https://aclanthology.org/2021.emnlp-main.478/
Findings-Long
1、Attention Weightsin Transformer NMT Fail Aligning Words Between Sequences but Largely ExplainModel Predictions
https://aclanthology.org/2021.findings-emnlp.39/
2、How Suitable AreSubword Segmentation Strategies for Translating Non-Concatenative Morphology?
https://aclanthology.org/2021.findings-emnlp.60/
3、ExploitingCurriculum Learning in Unsupervised Neural Machine Translation
https://aclanthology.org/2021.findings-emnlp.79/
4、MultilingualNeural Machine Translation: Can Linguistic Hierarchies Help?
https://aclanthology.org/2021.findings-emnlp.114/
5、ModelingConcentrated Cross-Attention for Neural Machine Translation with GaussianMixture Model
https://aclanthology.org/2021.findings-emnlp.121/
6、Competence-basedCurriculum Learning for Multilingual Machine Translation
https://aclanthology.org/2021.findings-emnlp.212/
7、MultilingualTranslation via Grafting Pre-trained Language Models
https://aclanthology.org/2021.findings-emnlp.233/
8、Counter-InterferenceAdapter for Multilingual Machine Translation
https://aclanthology.org/2021.findings-emnlp.240/
9、ImprovingMultilingual Neural Machine Translation with Auxiliary Source Languages
https://aclanthology.org/2021.findings-emnlp.260/
10、Bandits Don'tFollow Rules: Balancing Multi-Facet Machine Translation with Multi-ArmedBandits
https://aclanthology.org/2021.findings-emnlp.274/
11、Sometimes WeWant Ungrammatical Translations
https://aclanthology.org/2021.findings-emnlp.275/
12、The Low-ResourceDouble Bind: An Empirical Study of Pruning for Low-Resource Machine Translation
https://aclanthology.org/2021.findings-emnlp.282/
13、Token-wiseCurriculum Learning for Neural Machine Translation
https://aclanthology.org/2021.findings-emnlp.310/
14、RevisitingRobust Neural Machine Translation: A Transformer Case Study
https://aclanthology.org/2021.findings-emnlp.323/
15、Uncertainty-AwareMachine Translation Evaluation
https://aclanthology.org/2021.findings-emnlp.330/
16、Beyond Glass-BoxFeatures: Uncertainty Quantification Enhanced Quality Estimation for NeuralMachine Translation
https://aclanthology.org/2021.findings-emnlp.401/
Findings-Short
1、Mixup Decodingfor Diverse Machine Translation
https://aclanthology.org/2021.findings-emnlp.29/
2、Stream-levelLatency Evaluation for Simultaneous Machine Translation
https://aclanthology.org/2021.findings-emnlp.58/
3、On theComplementarity between Pre-Training and Back-Translation for Neural MachineTranslation
https://aclanthology.org/2021.findings-emnlp.247/
4、Sequence-to-LatticeModels for Fast Translation
https://aclanthology.org/2021.findings-emnlp.318/
5、Non-ParametricUnsupervised Domain Adaptation for Neural Machine Translation
https://aclanthology.org/2021.findings-emnlp.358/
6、RethinkingZero-shot Neural Machine Translation: From a Perspective of Latent Variables
https://aclanthology.org/2021.findings-emnlp.366/
7、Secoco:Self-Correcting Encoding for Neural Machine Translation
https://aclanthology.org/2021.findings-emnlp.396/
 

多模态

主会议-Short
1、Cost-effectiveEnd-to-end Information Extraction for Semi-structured Document Images
https://aclanthology.org/2021.emnlp-main.271/
2、UnsupervisedMulti-View Post-OCR Error Correction With Language Models
https://aclanthology.org/2021.emnlp-main.680/
3、Finnish DialectIdentification: The Effect of Audio and Text
https://aclanthology.org/2021.emnlp-main.692/
4、Mutual-LearningImproves End-to-End Speech Translation
https://aclanthology.org/2021.emnlp-main.325/
5、Visual Goal-StepInference using wikiHow
https://aclanthology.org/2021.emnlp-main.165/
6、Voice Query AutoCompletion
https://aclanthology.org/2021.emnlp-main.68/
7、Improving Pre-trainedVision-and-Language Embeddings for Phrase Grounding
https://aclanthology.org/2021.emnlp-main.513/
8、It Is Not As GoodAs You Think! Evaluating Simultaneous Machine Translation on InterpretationData
https://aclanthology.org/2021.emnlp-main.537/
9、Speechformer:Reducing Information Loss in Direct Speech Translation
https://aclanthology.org/2021.emnlp-main.127/
10、Is “moby dick” aWhale or a Bird? Named Entities and Terminology in Speech Translation
https://aclanthology.org/2021.emnlp-main.128/
11、Caption EnrichedSamples for Improving Hateful Memes Detection
https://aclanthology.org/2021.emnlp-main.738/
12、Language-AlignedWaypoint (LAW) Supervision for Vision-and-Language Navigation in ContinuousEnvironments
https://aclanthology.org/2021.emnlp-main.328/
13、Multilingual andCross-Lingual Intent Detection from Spoken Data
https://aclanthology.org/2021.emnlp-main.591/
14、Vision MattersWhen It Should: Sanity Checking Multimodal Machine Translation Models
https://aclanthology.org/2021.emnlp-main.673/
15、SystematicGeneralization on gSCAN: What is Nearly Solved and What is Next?
https://aclanthology.org/2021.emnlp-main.166/
16、Effect of VisualExtensions on Natural Language Understanding in Vision-and-Language Models
https://aclanthology.org/2021.emnlp-main.167/
Findings-Long
1、Self-supervisedContrastive Cross-Modality Representation Learning for Spoken QuestionAnswering
https://aclanthology.org/2021.findings-emnlp.3/
2、Joint MultimediaEvent Extraction from Video and Article
https://aclanthology.org/2021.findings-emnlp.8/
3、Fine-grainedSemantic Alignment Network for Weakly Supervised Temporal Language Grounding
https://aclanthology.org/2021.findings-emnlp.9/
4、Cross-ModalRetrieval Augmentation for Multi-Modal Classification
https://aclanthology.org/2021.findings-emnlp.11/
5、GeneratingMammography Reports from Multi-view Mammograms with BERT
https://aclanthology.org/2021.findings-emnlp.15/
6、Visually GroundedConcept Composition
https://aclanthology.org/2021.findings-emnlp.20/
7、Reasoning VisualDialog with Sparse Graph Learning and Knowledge Transfer
https://aclanthology.org/2021.findings-emnlp.31/
8、What Does YourSmile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using QuantumProbability
https://aclanthology.org/2021.findings-emnlp.74/
9、Diversity andConsistency: Exploring Visual Question-Answer Pair Generation
https://aclanthology.org/2021.findings-emnlp.91/
10、Entity-levelCross-modal Learning Improves Multi-modal Machine Translation
https://aclanthology.org/2021.findings-emnlp.92/
11、Learning toGround Visual Objects for Visual Dialog
https://aclanthology.org/2021.findings-emnlp.93/
12、Which is Makingthe Contribution: Modulating Unimodal and Cross-modal Dynamics for MultimodalSentiment Analysis
https://aclanthology.org/2021.findings-emnlp.109/
13、InconsistencyMatters: A Knowledge-guided Dual-inconsistency Network for Multi-modal RumorDetection
https://aclanthology.org/2021.findings-emnlp.122/
14、Optimal NeuralProgram Synthesis from Multimodal Specifications
https://aclanthology.org/2021.findings-emnlp.146/
15、TowardsDeveloping a Multilingual and Code-Mixed Visual Question Answering System byKnowledge Distillation
https://aclanthology.org/2021.findings-emnlp.151/
16、Enhancing VisualDialog Questioner with Entity-based Strategy Learning and Augmented Guesser
https://aclanthology.org/2021.findings-emnlp.158/
17、LanguageResource Efficient Learning for Captioning
https://aclanthology.org/2021.findings-emnlp.162/
18、Retrieval,Analogy, and Composition: A framework for Compositional Generalization in ImageCaptioning
https://aclanthology.org/2021.findings-emnlp.171/
19、MIRTT: LearningMultimodal Interaction Representations from Trilinear Transformers for VisualQuestion Answering
https://aclanthology.org/2021.findings-emnlp.196/
20、DialogueTRM:Exploring Multi-Modal Emotional Dynamics in a Conversation
https://aclanthology.org/2021.findings-emnlp.229/
21、Wav-BERT:Cooperative Acoustic and Linguistic Representation Learning for Low-ResourceSpeech Recognition
https://aclanthology.org/2021.findings-emnlp.236/
22、Data EfficientMasked Language Modeling for Vision and Language
https://aclanthology.org/2021.findings-emnlp.259/
23、Visual Cues andError Correction for Translation Robustness
https://aclanthology.org/2021.findings-emnlp.271/
24、An animated picturesays at least a thousand words: Selecting Gif-based Replies in MultimodalDialog
https://aclanthology.org/2021.findings-emnlp.276/
25、SD-QA: SpokenDialectal Question Answering for the Real World
https://aclanthology.org/2021.findings-emnlp.281/
26、COSMic: ACoherence-Aware Generation Metric for Image Descriptions
https://aclanthology.org/2021.findings-emnlp.291/
27、MURAL:Multimodal, Multitask Representations Across Languages
https://aclanthology.org/2021.findings-emnlp.293/
28、MSD:Saliency-aware Knowledge Distillation for Multimodal Understanding
https://aclanthology.org/2021.findings-emnlp.302/
29、Detecting Framesin News Headlines and Lead Images in U.S. Gun Violence Coverage
https://aclanthology.org/2021.findings-emnlp.339/
30、Cleaning DirtyBooks: Post-OCR Processing for Previously Scanned Texts
https://aclanthology.org/2021.findings-emnlp.356/
31、FastCorrect 2:Fast Error Correction on Multiple Candidates for Automatic Speech Recognition
https://aclanthology.org/2021.findings-emnlp.367/
32、Character-basedPCFG Induction for Modeling the Syntactic Acquisition of Morphologically RichLanguages
https://aclanthology.org/2021.findings-emnlp.371/
33、MOMENTA: AMultimodal Framework for Detecting Harmful Memes and Their Targets
https://aclanthology.org/2021.findings-emnlp.379/
34、NICE: NeuralImage Commenting with Empathy
https://aclanthology.org/2021.findings-emnlp.380/
Findings-Short
1、ProgressiveTransformer-Based Generation of Radiology Reports
https://aclanthology.org/2021.findings-emnlp.241/
2、Coreference-awareSurprisal Predicts Brain Response
https://aclanthology.org/2021.findings-emnlp.285/
3、Model-basedanalysis of brain activity reveals the hierarchy of language in 305 subjects
https://aclanthology.org/2021.findings-emnlp.308/
4、Weakly SupervisedContrastive Learning for Chest X-Ray Report Generation
https://aclanthology.org/2021.findings-emnlp.336/
5、DoesVision-and-Language Pretraining Improve Lexical Grounding?
https://aclanthology.org/2021.findings-emnlp.370/
6、QACE: AskingQuestions to Evaluate an Image Caption
https://aclanthology.org/2021.findings-emnlp.395/
7、Data-EfficientLanguage Shaped Few-shot Image Classification
https://aclanthology.org/2021.findings-emnlp.400/

摘要

主会议-Short
1、ConsideringNested Tree Structure in Sentence Extractive Summarization with Pre-trainedTransformer
https://aclanthology.org/2021.emnlp-main.330/
2、FrameSemantic-Enhanced Sentence Modeling for Sentence-level Extractive TextSummarization
https://aclanthology.org/2021.emnlp-main.331/
3、Sparsity andSentence Structure in Encoder-Decoder Attention of Summarization Systems
https://aclanthology.org/2021.emnlp-main.739/
4、Multiplex GraphNeural Network for Extractive Text Summarization
https://aclanthology.org/2021.emnlp-main.11/
5、A Bag of Tricksfor Dialogue Summarization
https://aclanthology.org/2021.emnlp-main.631/
6、IntegratingSemantic Scenario and Word Relations for Abstractive Sentence Summarization
https://aclanthology.org/2021.emnlp-main.196/
7、HETFORMER:Heterogeneous Transformer with Sparse Attention for Long-Text ExtractiveSummarization
https://aclanthology.org/2021.emnlp-main.13/
8、Gradient-BasedAdversarial Factual Consistency Evaluation for Abstractive Summarization
https://aclanthology.org/2021.emnlp-main.337/
Findings-Long
1、FactualConsistency Evaluation for Text Summarization via Counterfactual Estimation
https://aclanthology.org/2021.findings-emnlp.10/
2、ImprovingAbstractive Dialogue Summarization with Hierarchical Pretraining and TopicSegment
https://aclanthology.org/2021.findings-emnlp.97/
3、Topic-AwareContrastive Learning for Abstractive Dialogue Summarization
https://aclanthology.org/2021.findings-emnlp.106/
4、A Finer-grainUniversal Dialogue Semantic Structures based Model For Abstractive DialogueSummarization
https://aclanthology.org/2021.findings-emnlp.117/
5、Topic-GuidedAbstractive Multi-Document Summarization
https://aclanthology.org/2021.findings-emnlp.126/
6、MiRANews: Datasetand Benchmarks for Multi-Resource-Assisted News Summarization
https://aclanthology.org/2021.findings-emnlp.133/
7、Are Factuality CheckersReliable? Adversarial Meta-evaluation of Factuality in Summarization
https://aclanthology.org/2021.findings-emnlp.179/
8、Give the Truth:Incorporate Semantic Slot into Abstractive Dialogue Summarization
https://aclanthology.org/2021.findings-emnlp.209/
9、Novel NaturalLanguage Summarization of Program Code via Leveraging Multiple InputRepresentations
https://aclanthology.org/2021.findings-emnlp.214/
10、RetrievalAugmented Code Generation and Summarization
https://aclanthology.org/2021.findings-emnlp.232/
11、Post-EditingExtractive Summaries by Definiteness Prediction
https://aclanthology.org/2021.findings-emnlp.312/
12、LeveragingPretrained Models for Automatic Summarization of Doctor-Patient Conversations
https://aclanthology.org/2021.findings-emnlp.313/
13、ConvexAggregation for Opinion Summarization
https://aclanthology.org/2021.findings-emnlp.328/
Findings-Short
1、Using QuestionAnswering Rewards to Improve Abstractive Summarization
https://aclanthology.org/2021.findings-emnlp.47/
2、Exploring MultitaskLearning for Low-Resource Abstractive Summarization
https://aclanthology.org/2021.findings-emnlp.142/
3、Mitigating DataScarceness through Data Synthesis, Augmentation and Curriculum for AbstractiveSummarization
https://aclanthology.org/2021.findings-emnlp.175/
4、Does Pretrainingfor Summarization Require Knowledge Transfer?
https://aclanthology.org/2021.findings-emnlp.273/
5、LeveragingInformation Bottleneck for Scientific Document Summarization
https://aclanthology.org/2021.findings-emnlp.345/
6、An ExploratoryStudy on Long Dialogue Summarization: What Works and What's Next
https://aclanthology.org/2021.findings-emnlp.377/
 

文本生成

主会议-Short
1、GeneratingDatasets with Pretrained Language Models
https://aclanthology.org/2021.emnlp-main.555/
2、ReducingDiscontinuous to Continuous Parsing with Pointer Network Reordering
https://aclanthology.org/2021.emnlp-main.825/
3、Injecting EntityTypes into Entity-Guided Text Generation
https://aclanthology.org/2021.emnlp-main.56/
4、Smelting Gold andSilver for Improved Multilingual AMR-to-Text Generation
https://aclanthology.org/2021.emnlp-main.57/
5、Open-domainclarification question generation without question examples
https://aclanthology.org/2021.emnlp-main.44/
6、Implicit PremiseGeneration with Discourse-aware Commonsense Knowledge Models
https://aclanthology.org/2021.emnlp-main.504/
7、Transformer-basedLexically Constrained Headline Generation
https://aclanthology.org/2021.emnlp-main.335/
8、NeuTral Rewriter:A Rule-Based and Neural Approach to Automatic Rewriting into Gender NeutralAlternatives
https://aclanthology.org/2021.emnlp-main.704/
Findings-Long
1、KeyphraseGeneration with Fine-Grained Evaluation-Guided Reinforcement Learning
https://aclanthology.org/2021.findings-emnlp.45/
2、Plan-then-Generate:Controlled Data-to-Text Generation via Planning
https://aclanthology.org/2021.findings-emnlp.76/
3、TowardsDocument-Level Paraphrase Generation with Sentence Rewriting and Reordering
https://aclanthology.org/2021.findings-emnlp.89/
4、Simulated Chatsfor Building Dialog Systems: Learning to Generate Conversations fromInstructions
https://aclanthology.org/2021.findings-emnlp.103/
5、TWT: Table withWritten Text for Controlled Data-to-Text Generation
https://aclanthology.org/2021.findings-emnlp.107/
6、CombiningCurriculum Learning and Knowledge Distillation for Dialogue Generation
https://aclanthology.org/2021.findings-emnlp.111/
7、SciXGen: AScientific Paper Dataset for Context-Aware Text Generation
https://aclanthology.org/2021.findings-emnlp.128/
8、Entity-BasedSemantic Adequacy for Data-to-Text Generation
https://aclanthology.org/2021.findings-emnlp.132/
9、HypoGen:Hyperbole Generation with Commonsense and Counterfactual Knowledge
https://aclanthology.org/2021.findings-emnlp.136/
10、Grouped-Attentionfor Content-Selection and Content-Plan Generation
https://aclanthology.org/2021.findings-emnlp.166/
11、TURINGBENCH: ABenchmark Environment for Turing Test in the Age of Neural Text Generation
https://aclanthology.org/2021.findings-emnlp.172/
12、proScript:Partially Ordered Scripts Generation
https://aclanthology.org/2021.findings-emnlp.184/
13、SideControl:Controlled Open-domain Dialogue Generation via Additive Side Networks
https://aclanthology.org/2021.findings-emnlp.188/
14、Attribute Alignment:Controlling Text Generation from Pre-trained Language Models
https://aclanthology.org/2021.findings-emnlp.194/
15、Generate &Rank: A Multi-task Framework for Math Word Problems
https://aclanthology.org/2021.findings-emnlp.195/
16、InformedSampling for Diversity in Concept-to-Text NLG
https://aclanthology.org/2021.findings-emnlp.213/
17、Automatic rulegeneration for time expression normalization
https://aclanthology.org/2021.findings-emnlp.269/
18、Learning andAnalyzing Generation Order for Undirected Sequence Models
https://aclanthology.org/2021.findings-emnlp.298/
19、GeneratingRealistic Natural Language Counterfactuals
https://aclanthology.org/2021.findings-emnlp.306/
20、A Plug-and-PlayMethod for Controlled Text Generation
https://aclanthology.org/2021.findings-emnlp.334/
21、Attend, Memorizeand Generate: Towards Faithful Table-to-Text Generation in Few Shots
https://aclanthology.org/2021.findings-emnlp.347/
22、HAConvGNN:Hierarchical Attention Based Convolutional Graph Neural Network for CodeDocumentation Generation in Jupyter Notebooks
https://aclanthology.org/2021.findings-emnlp.381/
23、Simple orComplex? Complexity-controllable Question Generation with Soft Templates andDeep Mixture of Experts Model
https://aclanthology.org/2021.findings-emnlp.397/
24、GeDi: GenerativeDiscriminator Guided Decoding for Faster Controllable Sequence Generation
https://aclanthology.org/2021.findings-emnlp.424/
Findings-Short
1、Few-ShotTable-to-Text Generation with Prototype Memory
https://aclanthology.org/2021.findings-emnlp.77/
2、Geo-BERTPre-training Model for Query Rewriting in POI Search
https://aclanthology.org/2021.findings-emnlp.190/
3、SciCap:Generating Captions for Scientific Figures
https://aclanthology.org/2021.findings-emnlp.277/
4、How May I HelpYou? Using Neural Text Simplification to Improve Downstream NLP Tasks
https://aclanthology.org/2021.findings-emnlp.343/
5、Detect andPerturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-basedDecoding
https://aclanthology.org/2021.findings-emnlp.352/
6、Improving TextAuto-Completion with Next Phrase Prediction
https://aclanthology.org/2021.findings-emnlp.378/
7、A Multi-labelMulti-hop Relation Detection Model based on Relation-aware Sequence Generation
https://aclanthology.org/2021.findings-emnlp.404/
8、ContrastiveRepresentation Learning for Exemplar-Guided Paraphrase Generation
https://aclanthology.org/2021.findings-emnlp.409/
 

文本风格改写

主会议-Short
1、Preventing AuthorProfiling through Zero-Shot Multilingual Back-Translation
https://aclanthology.org/2021.emnlp-main.684/
2、Does BERT Learnas Humans Perceive? Understanding Linguistic Styles through Lexica
https://aclanthology.org/2021.emnlp-main.510/
3、ExploringNon-Autoregressive Text Style Transfer
https://aclanthology.org/2021.emnlp-main.730/
Findings-Long
1、RethinkingSentiment Style Transfer
https://aclanthology.org/2021.findings-emnlp.135/
2、DisentanglingGenerative Factors in Natural Language with Discrete Variational Autoencoders
https://aclanthology.org/2021.findings-emnlp.301/
 

推理

主会议-Short
1、ExploringStrategies for Generalizable Commonsense Reasoning with Pre-trained Models
https://aclanthology.org/2021.emnlp-main.445/
2、ContinuousEntailment Patterns for Lexical Inference in Context
https://aclanthology.org/2021.emnlp-main.556/
3、Towards Zero-shotCommonsense Reasoning with Self-supervised Refinement of Language Models
https://aclanthology.org/2021.emnlp-main.688/
Findings-Long
1、KFCNet: KnowledgeFiltering and Contrastive Learning for Generative Commonsense Reasoning
https://aclanthology.org/2021.findings-emnlp.249/
2、NeuralUnification for Logic Reasoning over Natural Language
https://aclanthology.org/2021.findings-emnlp.331/
3、ImprovingUnsupervised Commonsense Reasoning Using Knowledge-Enabled Natural LanguageInference
https://aclanthology.org/2021.findings-emnlp.420/
Findings-Short
1、Effect GenerationBased on Causal Reasoning
https://aclanthology.org/2021.findings-emnlp.48/

模型鲁棒性及对抗

主会议-Short
1、ReconstructionAttack on Instance Encoding for Language Understanding
https://aclanthology.org/2021.emnlp-main.154/
2、KnowMAN: WeaklySupervised Multinomial Adversarial Networks
https://aclanthology.org/2021.emnlp-main.751/
3、Don't Search fora Search Method --- Simple Heuristics Suffice for Adversarial Text Attacks
https://aclanthology.org/2021.emnlp-main.647/
4、ONION: A Simpleand Effective Defense Against Textual Backdoor Attacks
https://aclanthology.org/2021.emnlp-main.752/
Findings-Long
1、BFClass: ABackdoor-free Text Classification Framework
https://aclanthology.org/2021.findings-emnlp.40/
2、How to Select OneAmong All ? An Empirical Study Towards the Robustness of Knowledge Distillationin Natural Language Understanding
https://aclanthology.org/2021.findings-emnlp.65/
3、Towards ImprovingAdversarial Training of NLP Models
https://aclanthology.org/2021.findings-emnlp.81/
4、APGN: Adversarialand Parameter Generation Networks for Multi-Source Cross-Domain DependencyParsing
https://aclanthology.org/2021.findings-emnlp.149/
5、TAG: GradientAttack on Transformer-based Language Models
https://aclanthology.org/2021.findings-emnlp.305/
6、Gated Transformerfor Robust De-noised Sequence-to-Sequence Modelling
https://aclanthology.org/2021.findings-emnlp.309/
7、ARCH: EfficientAdversarial Regularized Training with Caching
https://aclanthology.org/2021.findings-emnlp.348/
8、Influence Tuning:Demoting Spurious Correlations via Instance Attribution and Instance-DrivenUpdates
https://aclanthology.org/2021.findings-emnlp.374/
9、SyntacticallyDiverse Adversarial Network for Knowledge-Grounded Conversation Generation
https://aclanthology.org/2021.findings-emnlp.394/
10、CounterfactualAdversarial Learning with Representation Interpolation
https://aclanthology.org/2021.findings-emnlp.413/
Findings-Short
1、AdversarialExamples for Evaluating Math Word Problem Solvers
https://aclanthology.org/2021.findings-emnlp.230/
2、Mitigating DataPoisoning in Text Classification with Differential Privacy
https://aclanthology.org/2021.findings-emnlp.369/
3、Counter-ContrastiveLearning for Language GANs
https://aclanthology.org/2021.findings-emnlp.415/
 

模型压缩

主会议-Short
1、Students WhoStudy Together Learn Better: On the Importance of Collective KnowledgeDistillation for Domain Transfer in Fact Verification
https://aclanthology.org/2021.emnlp-main.558/
Findings-Long
1、EfficientBERT:Progressively Searching Multilayer Perceptron via Warm-up KnowledgeDistillation
https://aclanthology.org/2021.findings-emnlp.123/
2、BeyondDistillation: Task-level Mixture-of-Experts for Efficient Inference
https://aclanthology.org/2021.findings-emnlp.304/
Findings-Short
1、RW-KD:Sample-wise Loss Terms Re-Weighting for Knowledge Distillation
https://aclanthology.org/2021.findings-emnlp.270/

小样本

主会议-Short
1、Language Modelsare Few-Shot Butlers
https://aclanthology.org/2021.emnlp-main.734/
2、AvoidingInference Heuristics in Few-shot Prompt-based Finetuning
https://aclanthology.org/2021.emnlp-main.713/
3、Bridge to TargetDomain by Prototypical Contrastive Learning and Label Confusion: Re-exploreZero-Shot Learning for Slot Filling
https://aclanthology.org/2021.emnlp-main.746/
4、To Share or notto Share: Predicting Sets of Sources for Model Transfer Learning
https://aclanthology.org/2021.emnlp-main.689/
5、FrustratinglySimple but Surprisingly Strong: Using Language-Independent Features forZero-shot Cross-lingual Semantic Parsing
https://aclanthology.org/2021.emnlp-main.472/
6、Few-Shot IntentDetection via Contrastive Pre-Training and Fine-Tuning
https://aclanthology.org/2021.emnlp-main.144/
7、Nearest NeighbourFew-Shot Learning for Cross-lingual Classification
https://aclanthology.org/2021.emnlp-main.131/
Findings-Long
1、Beyond Reptile:Meta-Learned Dot-Product Maximization between Gradients for ImprovedSingle-Task Regularization
https://aclanthology.org/2021.findings-emnlp.37/
2、Saliency-basedMulti-View Mixed Language Training for Zero-shot Cross-lingual Classification
https://aclanthology.org/2021.findings-emnlp.55/
3、LearnContinually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shotLearning
https://aclanthology.org/2021.findings-emnlp.62/
4、An Explicit-Jointand Supervised-Contrastive Learning Framework for Few-Shot IntentClassification and Slot Filling
https://aclanthology.org/2021.findings-emnlp.167/
5、Adapting LanguageModels for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections
https://aclanthology.org/2021.findings-emnlp.244/
6、BenchmarkingMeta-embeddings: What Works and What Does Not
https://aclanthology.org/2021.findings-emnlp.333/
Findings-Short
1、Efficient TestTime Adapter Ensembling for Low-resource Language Varieties
https://aclanthology.org/2021.findings-emnlp.63/
2、Effectiveness ofPre-training for Few-shot Intent Classification
https://aclanthology.org/2021.findings-emnlp.96/
3、Self-Trainingusing Rules of Grammar for Few-Shot NLU
https://aclanthology.org/2021.findings-emnlp.389/

知识表征

主会议-Short
1、Enhancing theContext Representation in Similarity-based Word Sense Disambiguation
https://aclanthology.org/2021.emnlp-main.706/
2、AugmentingBERT-style Models with Predictive Coding to Improve Discourse-levelRepresentations
https://aclanthology.org/2021.emnlp-main.240/
3、AligningCross-lingual Sentence Representations with Dual Momentum Contrast
https://aclanthology.org/2021.emnlp-main.309/
4、A NewRepresentation for Span-based CCG Parsing
https://aclanthology.org/2021.emnlp-main.826/
5、ExaminingCross-lingual Contextual Embeddings with Orthogonal Structural Probes
https://aclanthology.org/2021.emnlp-main.376/
6、ExploitingTwitter as Source of Large Corpora of Weakly Similar Pairs for SemanticSentence Embeddings
https://aclanthology.org/2021.emnlp-main.780/
7、LearningUniversal Authorship Representations
https://aclanthology.org/2021.emnlp-main.70/
Findings-Long
1、TSDAE: UsingTransformer-based Sequential Denoising Auto-Encoderfor Unsupervised SentenceEmbedding Learning
https://aclanthology.org/2021.findings-emnlp.59/
2、Char2Subword:Extending the Subword Embedding Space Using Robust Character Compositionality
https://aclanthology.org/2021.findings-emnlp.141/
3、SemanticAlignment with Calibrated Similarity for Multilingual Sentence Embedding
https://aclanthology.org/2021.findings-emnlp.153/
4、UnClE: ExplicitlyLeveraging Semantic Similarity to Reduce the Parameters of Word Embeddings
https://aclanthology.org/2021.findings-emnlp.156/
5、DetectingPolarized Topics Using Partisanship-aware Contextualized Topic Embeddings
https://aclanthology.org/2021.findings-emnlp.181/
6、Refining BERTEmbeddings for Document Hashing via Mutual Information Maximization
https://aclanthology.org/2021.findings-emnlp.203/
7、A ComprehensiveComparison of Word Embeddings in Event & Entity Coreference Resolution.
https://aclanthology.org/2021.findings-emnlp.235/
8、LocalityPreserving Sentence Encoding
https://aclanthology.org/2021.findings-emnlp.262/
9、KnowledgeRepresentation Learning with Contrastive Completion Coding
https://aclanthology.org/2021.findings-emnlp.263/
10、ContrastiveDocument Representation Learning with Graph Attention Networks
https://aclanthology.org/2021.findings-emnlp.327/
11、Block-wise WordEmbedding Compression Revisited: Better Weighting and Structuring
https://aclanthology.org/2021.findings-emnlp.372/
12、HOTTER:Hierarchical Optimal Topic Transport with Explanatory Context Representations
https://aclanthology.org/2021.findings-emnlp.418/
Findings-Short
1、WhiteningBERT: AnEasy Unsupervised Sentence Embedding Approach
https://aclanthology.org/2021.findings-emnlp.23/
2、SupCL-Seq:Supervised Contrastive Learning for Downstream Optimized SequenceRepresentations
https://aclanthology.org/2021.findings-emnlp.289/
 

多语言

主会议-Short
1、A Simple andEffective Method To Eliminate the Self Language Bias in MultilingualRepresentations
https://aclanthology.org/2021.emnlp-main.470/
2、Discrete and SoftPrompting for Multilingual Models
https://aclanthology.org/2021.emnlp-main.672/
3、Learning CompactMetrics for MT
https://aclanthology.org/2021.emnlp-main.58/
Findings-Long
1、An UnsupervisedMethod for Building Sentence Simplification Corpora in Multiple Languages
https://aclanthology.org/2021.findings-emnlp.22/
2、MultilingualChart-based Constituency Parse Extraction from Pre-trained Language Models
https://aclanthology.org/2021.findings-emnlp.41/
3、DiscoveringRepresentation Sprachbund For Multilingual Pre-Training
https://aclanthology.org/2021.findings-emnlp.75/
4、A ConditionalGenerative Matching Model for Multi-lingual Reply Suggestion
https://aclanthology.org/2021.findings-emnlp.134/
5、Subword Mappingand Anchoring across Languages
https://aclanthology.org/2021.findings-emnlp.224/
6、Translate &Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data
https://aclanthology.org/2021.findings-emnlp.279/
7、mDAPT:Multilingual Domain Adaptive Pretraining in a Single Model
https://aclanthology.org/2021.findings-emnlp.290/
8、Using OptimalTransport as Alignment Objective for fine-tuning Multilingual ContextualizedEmbeddings
https://aclanthology.org/2021.findings-emnlp.329/
9、MAD-G:Multilingual Adapter Generation for Efficient Cross-Lingual Transfer
https://aclanthology.org/2021.findings-emnlp.410/
Findings-Short
1、LanguageClustering for Multilingual Named Entity Recognition
https://aclanthology.org/2021.findings-emnlp.4/
2、Switch Pointbiased Self-Training: Re-purposing Pretrained Models for Code-Switching
https://aclanthology.org/2021.findings-emnlp.373/

社会道德伦理偏见

主会议-Short
1、SociolectalAnalysis of Pretrained Language Models
https://aclanthology.org/2021.emnlp-main.375/
2、CAPE:Context-Aware Private Embeddings for Private Language Learning
https://aclanthology.org/2021.emnlp-main.628/
3、When differentialprivacy meets NLP: The devil is in the detail
https://aclanthology.org/2021.emnlp-main.114/
4、Does SocialPressure Drive Persuasion in Online Fora?
https://aclanthology.org/2021.emnlp-main.725/
5、CRYPTOGRU: LowLatency Privacy-Preserving Text Analysis With GRU
https://aclanthology.org/2021.emnlp-main.156/
6、Lying ThroughOne's Teeth: A Study on Verbal Leakage Cues
https://aclanthology.org/2021.emnlp-main.370/
Findings-Long
1、Improving PrivacyGuarantee and Efficiency of Latent Dirichlet Allocation Model Training UnderDifferential Privacy
https://aclanthology.org/2021.findings-emnlp.14/
2、Mining the Causeof Political Decision-Making from Social Media: A Case Study of COVID-19Policies across the US States
https://aclanthology.org/2021.findings-emnlp.27/
3、Fighting theCOVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers,Social Media Platforms, Policy Makers, and the Society
https://aclanthology.org/2021.findings-emnlp.56/
4、To Protect and ToServe? Analyzing Entity-Centric Framing of Police Violence
https://aclanthology.org/2021.findings-emnlp.82/
5、Learning andEvaluating a Differentially Private Pre-trained Language Model
https://aclanthology.org/2021.findings-emnlp.102/
6、An unsupervisedframework for tracing textual sources of moral change
https://aclanthology.org/2021.findings-emnlp.105/
7、Uni-FedRec: AUnified Privacy-Preserving News Recommendation Framework for Model Training andOnline Serving
https://aclanthology.org/2021.findings-emnlp.124/
8、Say 'YES' toPositivity: Detecting Toxic Language in Workplace Communications
https://aclanthology.org/2021.findings-emnlp.173/
9、TemporalAdaptation of BERT and Performance on Downstream Document Classification:Insights from Social Media
https://aclanthology.org/2021.findings-emnlp.206/
10、"Be nice toyour wife! The restaurants are closed": Can Gender Stereotype DetectionImprove Sexism Classification?
https://aclanthology.org/2021.findings-emnlp.242/
11、Modeling Usersand Online Communities for Abuse Detection: A Position on Ethics andExplainability
https://aclanthology.org/2021.findings-emnlp.287/
12、PredictingAnti-Asian Hateful Users on Twitter during COVID-19
https://aclanthology.org/2021.findings-emnlp.398/
13、SustainableModular Debiasing of Language Models
https://aclanthology.org/2021.findings-emnlp.411/
Findings-Short
1、fBERT: A NeuralTransformer for Identifying Offensive Content
https://aclanthology.org/2021.findings-emnlp.154/
2、Unpacking theInterdependent Systems of Discrimination: Ableist Bias in NLP Systems throughan Intersectional Lens
https://aclanthology.org/2021.findings-emnlp.267/
3、A ComputationalExploration of Pejorative Language in Social Media
https://aclanthology.org/2021.findings-emnlp.296/
4、Towards AutomaticBias Detection in Knowledge Graphs
https://aclanthology.org/2021.findings-emnlp.321/
5、UncoveringImplicit Gender Bias in Narratives through Commonsense Inference
https://aclanthology.org/2021.findings-emnlp.326/
6、Fight Fire withFire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech
https://aclanthology.org/2021.findings-emnlp.402/
7、Don't Discard Allthe Biased Instances: Investigating a Core Assumption in Dataset BiasMitigation Techniques
https://aclanthology.org/2021.findings-emnlp.405/

虚假新闻检测

Findings-Long
1、Active Learningfor Rumor Identification on Social Media
https://aclanthology.org/2021.findings-emnlp.387/

指代、链指、消歧及对齐

主会议-Short
1、Highly ParallelAutoregressive Entity Linking with Discriminative Correction
https://aclanthology.org/2021.emnlp-main.604/
2、MuVER: ImprovingFirst-Stage Entity Retrieval with Multi-View Entity Representations
https://aclanthology.org/2021.emnlp-main.205/
3、IntegratingPersonalized PageRank into Neural Word Sense Disambiguation
https://aclanthology.org/2021.emnlp-main.715/
4、Large-ScaleRelation Learning for Question Answering over Knowledge Bases with Pre-trainedLanguage Models
https://aclanthology.org/2021.emnlp-main.296/
Findings-Long
1、Japanese ZeroAnaphora Resolution Can Benefit from Parallel Texts Through Neural TransferLearning
https://aclanthology.org/2021.findings-emnlp.165/
2、Named EntityRecognition for Entity Linking: What Works and What's Next
https://aclanthology.org/2021.findings-emnlp.220/
3、Improved WordSense Disambiguation with Enhanced Sense Representations
https://aclanthology.org/2021.findings-emnlp.365/
Findings-Short
1、LeveragingWord-Formation Knowledge for Chinese Word Sense Disambiguation
https://aclanthology.org/2021.findings-emnlp.78/
2、BERT might beOverkill: A Tiny but Effective Biomedical Entity Linker based on ResidualConvolutional Neural Networks
https://aclanthology.org/2021.findings-emnlp.140/
3、Cross-Domain DataIntegration for Named Entity Disambiguation in Biomedical Text
https://aclanthology.org/2021.findings-emnlp.388/
 

数据增强

主会议-Short
1、Good-EnoughExample Extrapolation
https://aclanthology.org/2021.emnlp-main.479/
2、EfficientContrastive Learning via Novel Data Augmentation and Curriculum Learning
https://aclanthology.org/2021.emnlp-main.138/
3、"So YouThink You're Funny?": Rating the Humour Quotient in Standup Comedy
https://aclanthology.org/2021.emnlp-main.789/
4、LevenshteinTraining for Word-level Quality Estimation
https://aclanthology.org/2021.emnlp-main.539/
Findings-Long
1、GPT3Mix:Leveraging Large-scale Language Models for Text Augmentation
https://aclanthology.org/2021.findings-emnlp.192/
Findings-Short
1、AEDA: An EasierData Augmentation Technique for Text Classification
https://aclanthology.org/2021.findings-emnlp.234/

纠错

主会议-Short
1、Is this the endof the gold standard? A straightforward reference-less grammatical errorcorrection metric
https://aclanthology.org/2021.emnlp-main.239/
2、Self-SupervisedCurriculum Learning for Spelling Error Correction
https://aclanthology.org/2021.emnlp-main.281/
3、SpellBERT: ALightweight Pretrained Model for Chinese Spelling Check
https://aclanthology.org/2021.emnlp-main.287/
Findings-Short
1、AnAlignment-Agnostic Model for Chinese Text Error Correction
https://aclanthology.org/2021.findings-emnlp.30/
2、BeyondGrammatical Error Correction: Improving L1-influenced research writing inEnglish using pre-trained encoder-decoder models
https://aclanthology.org/2021.findings-emnlp.216/
3、Grammatical ErrorCorrection with Contrastive Learning in Low Error Density Domains
https://aclanthology.org/2021.findings-emnlp.419/
 

图相关

主会议-Short
1、Improving QueryGraph Generation for Complex Question Answering over Knowledge Base
https://aclanthology.org/2021.emnlp-main.346/
2、Data Collectionvs. Knowledge Graph Completion: What is Needed to Improve Coverage?
https://aclanthology.org/2021.emnlp-main.501/
Findings-Long
1、Extracting Topicswith Simultaneous Word Co-occurrence and Semantic Correlation Graphs: NeuralTopic Modeling for Short Texts
https://aclanthology.org/2021.findings-emnlp.2/
2、P-INT: APath-based Interaction Model for Few-shot Knowledge Graph Completion
https://aclanthology.org/2021.findings-emnlp.35/
3、HyperbolicGeometry is Not Necessary: Lightweight Euclidean-Based Models forLow-Dimensional Knowledge Graph Embeddings
https://aclanthology.org/2021.findings-emnlp.42/
4、ImprovingKnowledge Graph Embedding Using Affine Transformations of EntitiesCorresponding to Each Relation
https://aclanthology.org/2021.findings-emnlp.46/
5、An Analysis ofEuclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from WordEmbedding Spaces
https://aclanthology.org/2021.findings-emnlp.64/
6、EventKE:Event-Enhanced Knowledge Graph Embedding
https://aclanthology.org/2021.findings-emnlp.120/
7、An Edge-EnhancedHierarchical Graph-to-Tree Network for Math Word Problem Solving
https://aclanthology.org/2021.findings-emnlp.127/
8、Context-awareEntity Typing in Knowledge Graphs
https://aclanthology.org/2021.findings-emnlp.193/
9、Open-DomainContextual Link Prediction and its Complementarity with Entailment Graphs
https://aclanthology.org/2021.findings-emnlp.238/
10、RelDiff:Enriching Knowledge Graph Relation Representations for SensitivityClassification
https://aclanthology.org/2021.findings-emnlp.311/
11、HyperExpan:Taxonomy Expansion with Hyperbolic Representation Learning
https://aclanthology.org/2021.findings-emnlp.353/
Findings-Short
1、A NeuralGraph-based Local Coherence Model
https://aclanthology.org/2021.findings-emnlp.199/
2、LearningNumeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph
https://aclanthology.org/2021.findings-emnlp.221/
3、HyperbolicHierarchy-Aware Knowledge Graph Embedding for Link Prediction
https://aclanthology.org/2021.findings-emnlp.251/
4、KLMo: KnowledgeGraph Enhanced Pretrained Language Model with Fine-Grained Relationships
https://aclanthology.org/2021.findings-emnlp.384/
5、Perceived andIntended Sarcasm Detection with Graph Attention Networks
https://aclanthology.org/2021.findings-emnlp.408/

文本分类

主会议-Short
1、Knowledge-AwareMeta-learning for Low-Resource Text Classification
https://aclanthology.org/2021.emnlp-main.136/
2、On Classifyingwhether Two Texts are on the Same Side of an Argument
https://aclanthology.org/2021.emnlp-main.795/
3、Balancing Methodsfor Multi-label Text Classification with Long-Tailed Class Distribution
https://aclanthology.org/2021.emnlp-main.643/
4、CoPHE: ACount-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label TextClassification
https://aclanthology.org/2021.emnlp-main.69/
Findings-Long
1、WHOSe Heritage:Classification of UNESCO World Heritage Statements of "OutstandingUniversal Value" with Soft Labels
https://aclanthology.org/2021.findings-emnlp.34/
2、Constructingcontrastive samples via summarization for text classification with limitedannotations
https://aclanthology.org/2021.findings-emnlp.118/
3、End-to-end NeuralInformation Status Classification
https://aclanthology.org/2021.findings-emnlp.119/
4、Cross-lingualTransfer for Text Classification with Dictionary-based Heterogeneous Graph
https://aclanthology.org/2021.findings-emnlp.130/
5、ConicalClassification For Efficient One-Class Topic Determination
https://aclanthology.org/2021.findings-emnlp.143/
6、Classificationand Geotemporal Analysis of Quality-of-Life Issues in Tenant Reviews
https://aclanthology.org/2021.findings-emnlp.217/
7、A multilabelapproach to morphosyntactic probing
https://aclanthology.org/2021.findings-emnlp.382/
Findings-Short
1、When in Doubt:Improving Classification Performance with Alternating Normalization
https://aclanthology.org/2021.findings-emnlp.148/
2、Devil's Advocate:Novel Boosting Ensemble Method from Psychological Findings for TextClassification
https://aclanthology.org/2021.findings-emnlp.187/
3、Beyond the Tip ofthe Iceberg: Assessing Coherence of Text Classifiers
https://aclanthology.org/2021.findings-emnlp.272/
 

NLP基础

主会议-Short
1、TowardsIncremental Transformers: An Empirical Analysis of Transformer Models forIncremental NLU
https://aclanthology.org/2021.emnlp-main.90/
2、FeedbackAttribution for Counterfactual Bandit Learning in Multi-Domain Spoken LanguageUnderstanding
https://aclanthology.org/2021.emnlp-main.91/
3、The Effect ofEfficient Messaging and Input Variability on Neural-Agent Iterated LanguageLearning
https://aclanthology.org/2021.emnlp-main.794/
4、Are Transformersa Modern Version of ELIZA? Observations on French Object Verb Agreement
https://aclanthology.org/2021.emnlp-main.377/
Findings-Long
1、CompositionalGeneralization via Semantic Tagging
https://aclanthology.org/2021.findings-emnlp.88/
2、Profiling NewsDiscourse Structure Using Explicit Subtopic Structures Guided Critics
https://aclanthology.org/2021.findings-emnlp.137/
3、Span PointerNetworks for Non-Autoregressive Task-Oriented Semantic Parsing
https://aclanthology.org/2021.findings-emnlp.161/
4、Translation asCross-Domain Knowledge: Attention Augmentation for Unsupervised Cross-DomainSegmenting and Labeling Tasks
https://aclanthology.org/2021.findings-emnlp.163/
5、Few-Shot NovelConcept Learning for Semantic Parsing
https://aclanthology.org/2021.findings-emnlp.177/
6、UnsupervisedDomain Adaptation Method with Semantic-Structural Alignment for DependencyParsing
https://aclanthology.org/2021.findings-emnlp.186/
7、Weakly SupervisedSemantic Parsing by Learning from Mistakes
https://aclanthology.org/2021.findings-emnlp.222/
8、Multilingual AMRParsing with Noisy Knowledge Distillation
https://aclanthology.org/2021.findings-emnlp.237/
9、Lexicon-BasedGraph Convolutional Network for Chinese Word Segmentation
https://aclanthology.org/2021.findings-emnlp.248/
10、A Corpus-basedSyntactic Analysis of Two-termed Unlike Coordination
https://aclanthology.org/2021.findings-emnlp.335/
11、Stacked AMRParsing with Silver Data
https://aclanthology.org/2021.findings-emnlp.406/
Findings-Short
1、SegmentingNatural Language Sentences via Lexical Unit Analysis
https://aclanthology.org/2021.findings-emnlp.18/
2、DetectingCompositionally Out-of-Distribution Examples in Semantic Parsing
https://aclanthology.org/2021.findings-emnlp.54/
3、UnsupervisedChunking as Syntactic Structure Induction with a Knowledge-Transfer Approach
https://aclanthology.org/2021.findings-emnlp.303/
4、Graph-BasedDecoding for Task Oriented Semantic Parsing
https://aclanthology.org/2021.findings-emnlp.341/

其他

主会议-Short
1、We Need to TalkAbout train-dev-test Splits
https://aclanthology.org/2021.emnlp-main.368/
2、Word-LevelCoreference Resolution
https://aclanthology.org/2021.emnlp-main.605/
3、On Homophony andRényi Entropy
https://aclanthology.org/2021.emnlp-main.653/
4、AnInformation-Theoretic Characterization of Morphological Fusion
https://aclanthology.org/2021.emnlp-main.793/
5、Value-awareApproximate Attention
https://aclanthology.org/2021.emnlp-main.753/
6、Neuro-SymbolicApproaches for Text-Based Policy Learning
https://aclanthology.org/2021.emnlp-main.245/
7、InducingStereotypical Character Roles from Plot Structure
https://aclanthology.org/2021.emnlp-main.39/
8、Fairness-awareClass Imbalanced Learning
https://aclanthology.org/2021.emnlp-main.155/
9、EvaluatingDebiasing Techniques for Intersectional Biases
https://aclanthology.org/2021.emnlp-main.193/
10、ParaphrasingCompound Nominalizations
https://aclanthology.org/2021.emnlp-main.632/
11、Neuro-SymbolicReinforcement Learning with First-Order Logic
https://aclanthology.org/2021.emnlp-main.283/
12、BiomedicalConcept Normalization by Leveraging Hypernyms
https://aclanthology.org/2021.emnlp-main.284/
13、What happens ifyou treat ordinal ratings as interval data? Human evaluations in NLP are evenmore under-powered than you think
https://aclanthology.org/2021.emnlp-main.703/
14、SWEAT: ScoringPolarization of Topics across Different Corpora
https://aclanthology.org/2021.emnlp-main.788/
15、A Secure andEfficient Federated Learning Framework for NLP
https://aclanthology.org/2021.emnlp-main.606/
16、“Average”Approximates “First Principal Component”? An Empirical Analysis onRepresentations from Neural Language Models
https://aclanthology.org/2021.emnlp-main.453/
17、Abstract,Rationale, Stance: A Joint Model for Scientific Claim Verification
https://aclanthology.org/2021.emnlp-main.290/
18、LifelongExplainer for Lifelong Learners
https://aclanthology.org/2021.emnlp-main.233/
19、Modeling HumanSentence Processing with Left-Corner Recurrent Neural Network Grammars
https://aclanthology.org/2021.emnlp-main.235/
20、Conditionalprobing: measuring usable information beyond a baseline
https://aclanthology.org/2021.emnlp-main.122/
21、BridgingPerception, Memory, and Inference through Semantic Relations
https://aclanthology.org/2021.emnlp-main.719/
Findings-Long
1、Neural NewsRecommendation with Collaborative News Encoding and Structural User Encoding
https://aclanthology.org/events/emnlp-2021/
2、CompositionalNetworks Enable Systematic Generalization for Grounded Language Understanding
https://aclanthology.org/2021.findings-emnlp.21/
3、Discourse-BasedSentence Splitting
https://aclanthology.org/2021.findings-emnlp.25/
4、CartographyActive Learning
https://aclanthology.org/2021.findings-emnlp.36/
5、FANATIC: FAstNoise-Aware TopIc Clustering
https://aclanthology.org/2021.findings-emnlp.57/
6、An Uncertainty-AwareEncoder for Aspect Detection
https://aclanthology.org/2021.findings-emnlp.69/
7、Learning toAnswer Psychological Questionnaire for Personality Detection
https://aclanthology.org/2021.findings-emnlp.98/
8、Generalization inText-based Games via Hierarchical Reinforcement Learning
https://aclanthology.org/2021.findings-emnlp.116/
9、Mapping Languageto Programs using Multiple Reward Components with Inverse ReinforcementLearning
https://aclanthology.org/2021.findings-emnlp.125/
10、Don't Miss thePotential Customers! Retrieving Similar Ads to Improve User Targeting
https://aclanthology.org/2021.findings-emnlp.129/
11、ProtoInfoMax:Prototypical Networks with Mutual Information Maximization for Out-of-DomainDetection
https://aclanthology.org/2021.findings-emnlp.138/
12、Sent2Span: SpanDetection for PICO Extraction in the Biomedical Text without Span Annotations
https://aclanthology.org/2021.findings-emnlp.147/
13、Natural SQL:Making SQL Easier to Infer from Natural Language Specifications
https://aclanthology.org/2021.findings-emnlp.174/
14、LeveragingBidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search
https://aclanthology.org/2021.findings-emnlp.191/
15、RollingLDA: AnUpdate Algorithm of Latent Dirichlet Allocation to Construct Consistent TimeSeries from Textual Data
https://aclanthology.org/2021.findings-emnlp.201/
16、MinimizingAnnotation Effort via Max-Volume Spectral Sampling
https://aclanthology.org/2021.findings-emnlp.246/
17、Comparinglearnability of two dependency schemes: ‘semantic’ (UD) and ‘syntactic’ (SUD)
https://aclanthology.org/2021.findings-emnlp.256/
18、ModelingMathematical Notation Semantics in Academic Papers
https://aclanthology.org/2021.findings-emnlp.266/
19、Self-SupervisedNeural Topic Modeling
https://aclanthology.org/2021.findings-emnlp.284/
20、Searching forMore Efficient Dynamic Programs
https://aclanthology.org/2021.findings-emnlp.322/
21、Table-based FactVerification With Salience-aware Learning
https://aclanthology.org/2021.findings-emnlp.338/
22、Textual TimeTravel: A Temporally Informed Approach to Theory of Mind
https://aclanthology.org/2021.findings-emnlp.351/
23、WrittenJustifications are Key to Aggregate Crowdsourced Forecasts
https://aclanthology.org/2021.findings-emnlp.355/
24、The TopicConfusion Task: A Novel Evaluation Scenario for Authorship Attribution
https://aclanthology.org/2021.findings-emnlp.359/
25、Micromodels forEfficient, Explainable, and Reusable Systems: A Case Study on Mental Health
https://aclanthology.org/2021.findings-emnlp.360/
26、A DeepDecomposable Model for Disentangling Syntax and Semantics in SentenceRepresentation
https://aclanthology.org/2021.findings-emnlp.364/
27、CompetingIndependent Modules for Knowledge Integration and Optimization
https://aclanthology.org/2021.findings-emnlp.376/
28、Co-TeachingStudent-Model through Submission Results of Shared Task
https://aclanthology.org/2021.findings-emnlp.383/
29、ComprehensivePunctuation Restoration for English and Polish
https://aclanthology.org/2021.findings-emnlp.393/
30、Fine-grainedTyping of Emerging Entities in Microblogs
https://aclanthology.org/2021.findings-emnlp.399/
31、Making Heads andTails of Models with Marginal Calibration for Sparse Tagsets
https://aclanthology.org/2021.findings-emnlp.423/
Findings-Short
1、Calibrate yourlisteners! Robust communication-based training for pragmatic speakers
https://aclanthology.org/2021.findings-emnlp.83/
2、ExploringDecomposition for Table-based Fact Verification
https://aclanthology.org/2021.findings-emnlp.90/
3、CVAE-basedRe-anchoring for Implicit Discourse Relation Classification
https://aclanthology.org/2021.findings-emnlp.110/
4、CompositionalData and Task Augmentation for Instruction Following
https://aclanthology.org/2021.findings-emnlp.178/
5、Cross-LingualLeveled Reading Based on Language-Invariant Features
https://aclanthology.org/2021.findings-emnlp.227/
6、Analysis ofLanguage Change in Collaborative Instruction Following
https://aclanthology.org/2021.findings-emnlp.239/
7、Argumentation-DrivenEvidence Association in Criminal Cases
https://aclanthology.org/2021.findings-emnlp.257/
8、Do UD Trees MatchMention Spans in Coreference Annotations?
https://aclanthology.org/2021.findings-emnlp.303/
9、ODIST: Open WorldClassification via Distributionally Shifted Instances
https://aclanthology.org/2021.findings-emnlp.316/
10、Multi-taskLearning to Enable Location Mention Identification in the Early Hours of aCrisis Event
https://aclanthology.org/2021.findings-emnlp.340/
11、ExpectedValidation Performance and Estimation of a Random Variable's Maximum
https://aclanthology.org/2021.findings-emnlp.342/
12、Learning TaskSampling Policy for Multitask Learning
https://aclanthology.org/2021.findings-emnlp.375/
13、Glyph EnhancedChinese Character Pre-Training for Lexical Sememe Prediction
https://aclanthology.org/2021.findings-emnlp.386/
14、MultiFix:Learning to Repair Multiple Errors by Optimal Alignment Learning
https://aclanthology.org/2021.findings-emnlp.417/

整理不易，请多多关注、转发、点赞。也请多多关注本人知乎「刘聪NLP」，有问题的朋友也欢迎加我微信私聊。

往期推荐



常用预训练语言模型（PTMs）总结

EMNLP2021主会议-656篇长文分类-附论文链接

一个使模型训练速度提升20%的Trick--BlockShuffle

EMNLP 2021之SF：一种预训练语言模型的片段微调（Span Fine-tuning）方法

EMNLP2021之AEDA：一种更简单的文本分类数据增强技术

回顾BART模型

授人以鱼不如授人以渔

ACL2021主会议论文汇总及分类

ACL2021 Findings论文汇总及分类

继续滑动看下一个

您可能也对以下帖子感兴趣

文章有问题？点此查看未经处理的缓存