---
title: "Conference of the North American Chapter of the… — works"
description: "324 works of Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) in our library · page 1 of 4, the most…"
canonical: "https://research.elamur.ai/free/en/venues/naacl/works/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/venues/naacl/works/)

[home](https://md.research.elamur.ai/free/en/) / [venues](https://md.research.elamur.ai/free/en/venues/) / [Conference of the North American Chapter of the…](https://md.research.elamur.ai/free/en/venues/naacl/) / works

# Works of Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)

324 works · page 1 of 4 · the most cited first · [← Conference of the North American Chapter of the…](https://md.research.elamur.ai/free/en/venues/naacl/)

**1** [2](https://md.research.elamur.ai/free/en/venues/naacl/works/page/2/) [3](https://md.research.elamur.ai/free/en/venues/naacl/works/page/3/) [4](https://md.research.elamur.ai/free/en/venues/naacl/works/page/4/) [next →](https://md.research.elamur.ai/free/en/venues/naacl/works/page/2/)

1. [**BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding**Jacob Devlin, Ming-Wei Chang, Kenton Lee, et al.*2019 · 23,422 citations*](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/)
2. [**Hierarchical Attention Networks for Document Classification**Zichao Yang, Diyi Yang, Chris Dyer, et al.*2016 · 5,409 citations*](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/)
3. [**fairseq: A Fast, Extensible Toolkit for Sequence Modeling**Myle Ott, Sergey Edunov, Alexei Baevski, et al.*2019 · 3,941 citations*](https://md.research.elamur.ai/free/en/papers/ott-2019-fairseq-fast-extensible-toolkit-sequence/)
4. [**Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network**Kristina Toutanova, Dan Klein, Christopher D. Manning, et al.*2003 · 3,559 citations*](https://md.research.elamur.ai/free/en/papers/toutanova-2003-feature-rich-part-speech-tagging/)
5. [**Linguistic Regularities in Continuous Space Word Representations**Tomas Mikolov, Wen-tau Yih, Geoffrey Zweig*2013 · 3,482 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2013-linguistic-regularities-continuous-space-word/)
6. [**Deep Contextualized Word Representations**Matthew E. Peters, Mark Neumann, Mohit Iyyer, et al.*2018 · 3,240 citations*](https://md.research.elamur.ai/free/en/papers/peters-2018-deep-contextualized-word-representations/)
7. [**A Diversity-Promoting Objective Function for Neural Conversation Models**Jiwei Li, Michel Galley, Chris Brockett, et al.*2016 · 2,925 citations*](https://md.research.elamur.ai/free/en/papers/li-2016-diversity-promoting-objective-function-neural/)
8. [**Self-Attention with Relative Position Representations**Peter Shaw, Jakob Uszkoreit, Ashish Vaswani*2018 · 2,811 citations*](https://md.research.elamur.ai/free/en/papers/shaw-2018-self-attention-relative-position-representations/)
9. [**FEVER: a Large-scale Dataset for Fact Extraction and VERification**James Thorne, Andreas Vlachos, Christos Christodoulopoulos, et al.*2018 · 2,205 citations*](https://md.research.elamur.ai/free/en/papers/thorne-2018-fever-large-scale-dataset-fact/)
10. [**Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics**Chin-Yew Lin, Eduard Hovy*2003 · 1,967 citations*](https://md.research.elamur.ai/free/en/papers/lin-2003-automatic-evaluation-summaries-n-gram/)
11. [**mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer**Linting Xue, Noah Constant, Adam Roberts, et al.*2021 · 1,574 citations*](https://md.research.elamur.ai/free/en/papers/xue-2021-mt5-massively-multilingual-pre-trained/)
12. [**A Probabilistic Earley Parser as a Psycholinguistic Model**John Hale*2001 · 1,448 citations*](https://md.research.elamur.ai/free/en/papers/hale-2001-probabilistic-earley-parser-as-psycholinguistic/)
13. [**BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions**Christopher Clark, Kenton Lee, Ming-Wei Chang, et al.*2019 · 1,299 citations*](https://md.research.elamur.ai/free/en/papers/clark-2019-boolq-exploring-surprising-difficulty-natural/)
14. [**A Structural Probe for Finding Syntax in Word Representations**John Hewitt, Christopher D. Manning*2019 · 1,228 citations*](https://md.research.elamur.ai/free/en/papers/hewitt-2019-structural-probe-finding-syntax-word/)
15. [**Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2018 · 1,186 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2018-gender-bias-coreference-resolution-evaluation/)
16. [**CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge**Alon Talmor, Jonathan Herzig, Nicholas Lourie, et al.*2019 · 1,146 citations*](https://md.research.elamur.ai/free/en/papers/talmor-2019-commonsenseqa-question-answering-challenge-targeting/)
17. [**It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners**Timo Schick, Hinrich Schütze*2021 · 1,118 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/)
18. [**A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches**Eneko Agirre, Enrique Alfonseca, Keith Hall, et al.*2009 · 1,117 citations*](https://md.research.elamur.ai/free/en/papers/agirre-2009-study-similarity-relatedness-distributional-wordnet/)
19. [**Predicting the Type and Target of Offensive Posts in Social Media**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2019 · 1,113 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2019-predicting-type-target-offensive-posts/)
20. [**Abstractive Sentence Summarization with Attentive Recurrent Neural Networks**Sumit Chopra, Michael Auli, Alexander M. Rush*2016 · 1,085 citations*](https://md.research.elamur.ai/free/en/papers/chopra-2016-abstractive-sentence-summarization-attentive-recurrent/)
21. [**Neural Architectures for Named Entity Recognition**Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, et al.*2016 · 1,021 citations*](https://md.research.elamur.ai/free/en/papers/lample-2016-neural-architectures-named-entity-recognition/)
22. [**A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents**Arman Cohan, Franck Dernoncourt, Doo Soon Kim, et al.*2018 · 1,018 citations*](https://md.research.elamur.ai/free/en/papers/cohan-2018-discourse-aware-attention-model-abstractive/)
23. [**Linguistic Knowledge and Transferability of Contextual Representations**Nelson F. Liu, Matt Gardner, Yonatan Belinkov, et al.*2019 · 1,011 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-linguistic-knowledge-transferability-contextual-representations/)
24. [**Attention is not Explanation**Sarthak Jain, Byron C. Wallace*2019 · 997 citations*](https://md.research.elamur.ai/free/en/papers/jain-2019-attention-is-not-explanation/)
25. [**PPDB: The Paraphrase Database**Juri Ganitkevitch, Benjamin Van Durme, Chris Callison-Burch*2013 · 964 citations*](https://md.research.elamur.ai/free/en/papers/ganitkevitch-2013-ppdb-paraphrase-database/)
26. [**Effective Use of Word Order for Text Categorization with Convolutional Neural Networks**Rie Johnson, Tong Zhang*2015 · 956 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2015-effective-use-word-order-text/)
27. [**A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network**Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, et al.*2018 · 944 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2018-novel-embedding-model-knowledge-base/)
28. [**DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs**Dheeru Dua, Yizhong Wang, Pradeep Dasigi, et al.*2019 · 941 citations*](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/)
29. [**Improved Inference for Unlexicalized Parsing**Slav Petrov, Dan Klein*2007 · 941 citations*](https://md.research.elamur.ai/free/en/papers/petrov-2007-improved-inference-unlexicalized-parsing/)
30. [**Adversarial Example Generation with Syntactically Controlled Paraphrase Networks**Mohit Iyyer, John Wieting, Kevin Gimpel, et al.*2018 · 936 citations*](https://md.research.elamur.ai/free/en/papers/iyyer-2018-adversarial-example-generation-syntactically-controlled/)
31. [**Are NLP Models really able to Solve Simple Math Word Problems?**Arkil Patel, Satwik Bhattamishra, Navin Goyal*2021 · 882 citations*](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/)
32. [**Joint Event Extraction via Recurrent Neural Networks**Thien Huu Nguyen, Kyunghyun Cho, Ralph Grishman*2016 · 881 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2016-joint-event-extraction-recurrent-neural/)
33. [**Gender Bias in Coreference Resolution**Rachel Rudinger, Jason Naradowsky, Brian Leonard, et al.*2018 · 852 citations*](https://md.research.elamur.ai/free/en/papers/rudinger-2018-gender-bias-coreference-resolution/)
34. [**Effective Self-Training for Parsing**David McClosky, Eugene Charniak, Mark Johnson*2006 · 846 citations*](https://md.research.elamur.ai/free/en/papers/mcclosky-2006-effective-self-training-parsing/)
35. [**Visualizing and Understanding Neural Models in NLP**Jiwei Li, Xinlei Chen, Eduard Hovy, et al.*2016 · 842 citations*](https://md.research.elamur.ai/free/en/papers/li-2016-visualizing-understanding-neural-models-nlp/)
36. [**Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features**Matteo Pagliardini, Prakhar Gupta, Martin Jaggi*2018 · 840 citations*](https://md.research.elamur.ai/free/en/papers/pagliardini-2018-unsupervised-learning-sentence-embeddings-compositional/)
37. [**Automatic Evaluation of Topic Coherence**David Newman, Jey Han Lau, Karl Grieser, et al.*2010 · 823 citations*](https://md.research.elamur.ai/free/en/papers/newman-2010-automatic-evaluation-topic-coherence/)
38. [**Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism**Orhan Firat, Kyunghyun Cho, Yoshua Bengio*2016 · 821 citations*](https://md.research.elamur.ai/free/en/papers/firat-2016-multi-way-multilingual-neural-machine/)
39. [**Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations**Sosuke Kobayashi*2018 · 804 citations*](https://md.research.elamur.ai/free/en/papers/kobayashi-2018-contextual-augmentation-data-augmentation-words/)
40. [**Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies**Max Grusky, Mor Naaman, Yoav Artzi*2018 · 778 citations*](https://md.research.elamur.ai/free/en/papers/grusky-2018-newsroom-dataset-1-3-million/)
41. [**On Measuring Social Biases in Sentence Encoders**Chandler May, Alex Wang, Shikha Bordia, et al.*2019 · 761 citations*](https://md.research.elamur.ai/free/en/papers/may-2019-measuring-social-biases-sentence-encoders/)
42. [**Colorless Green Recurrent Networks Dream Hierarchically**Kristina Gulordava, Piotr Bojanowski, Edouard Grave, et al.*2018 · 759 citations*](https://md.research.elamur.ai/free/en/papers/gulordava-2018-colorless-green-recurrent-networks-dream/)
43. [**Learning Distributed Representations of Sentences from Unlabelled Data**Felix Hill, Kyunghyun Cho, Anna Korhonen*2016 · 756 citations*](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-distributed-representations-sentences-unlabelled/)
44. [**Unified Pre-training for Program Understanding and Generation**Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, et al.*2021 · 746 citations*](https://md.research.elamur.ai/free/en/papers/ahmad-2021-unified-pre-training-program-understanding/)
45. [**What's in a translation rule?**Michel Galley, Mark Hopkins, Kevin Knight, et al.*2004 · 732 citations*](https://md.research.elamur.ai/free/en/papers/galley-2004-what-s-translation-rule/)
46. [**Learning To Retrieve Prompts for In-Context Learning**Ohad Rubin, Jonathan Herzig, Jonathan Berant*2021 · 717 citations*](https://md.research.elamur.ai/free/en/papers/rubin-2021-learning-retrieve-prompts-context-learning/)
47. [**Explainable Prediction of Medical Codes from Clinical Text**James Mullenbach, Sarah Wiegreffe, Jon Duke, et al.*2018 · 715 citations*](https://md.research.elamur.ai/free/en/papers/mullenbach-2018-explainable-prediction-medical-codes-clinical/)
48. [**Transfer Learning in Natural Language Processing**Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, et al.*2019 · 700 citations*](https://md.research.elamur.ai/free/en/papers/ruder-2019-transfer-learning-natural-language-processing/)
49. [**Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences**Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, et al.*2018 · 669 citations*](https://md.research.elamur.ai/free/en/papers/khashabi-2018-looking-beyond-surface-challenge-set/)
50. [**KILT: a Benchmark for Knowledge Intensive Language Tasks**Fabio Petroni, Aleksandra Piktus, Angela Fan, et al.*2020 · 663 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/)
51. [**Slot-Gated Modeling for Joint Slot Filling and Intent Prediction**Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, et al.*2018 · 653 citations*](https://md.research.elamur.ai/free/en/papers/goo-2018-slot-gated-modeling-joint-slot/)
52. [**Alignment by Agreement**Percy Liang, Ben Taskar, Dan Klein*2006 · 652 citations*](https://md.research.elamur.ai/free/en/papers/liang-2006-alignment-agreement/)
53. [**Chunking with Support Vector Machines**Taku Kudo, Yuji Matsumoto*2001 · 647 citations*](https://md.research.elamur.ai/free/en/papers/kudo-2001-chunking-support-vector-machines/)
54. [**FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP**Alan Akbik, Tanja Bergmann, Duncan Blythe, et al.*2019 · 645 citations*](https://md.research.elamur.ai/free/en/papers/akbik-2019-flair-easy-use-framework-state/)
55. [**Exploring Content Models for Multi-Document Summarization**Aria Haghighi, Lucy Vanderwende*2009 · 630 citations*](https://md.research.elamur.ai/free/en/papers/haghighi-2009-exploring-content-models-multi-document/)
56. [**A Frustratingly Easy Approach for Entity and Relation Extraction**Zexuan Zhong, Danqi Chen*2020 · 629 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/)
57. [**Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment**Regina Barzilay, Lillian Lee*2003 · 622 citations*](https://md.research.elamur.ai/free/en/papers/barzilay-2003-learning-paraphrase-unsupervised-approach-multiple/)
58. [**Higher-Order Coreference Resolution with Coarse-to-Fine Inference**Kenton Lee, Luheng He, Luke Zettlemoyer*2018 · 612 citations*](https://md.research.elamur.ai/free/en/papers/lee-2018-higher-order-coreference-resolution-coarse/)
59. [**RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering**Yingqi Qu, Yuchen Ding, Jing Liu, et al.*2021 · 601 citations*](https://md.research.elamur.ai/free/en/papers/qu-2021-rocketqa-optimized-training-approach-dense/)
60. [**FNet: Mixing Tokens with Fourier Transforms**James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, et al.*2022 · 593 citations*](https://md.research.elamur.ai/free/en/papers/thorp-2022-fnet-mixing-tokens-fourier-transforms/)
61. [**WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations**Mohammad Taher Pilehvar, Jose Camacho-Collados*2019 · 592 citations*](https://md.research.elamur.ai/free/en/papers/pilehvar-2019-wic-word-context-dataset-evaluating/)
62. [**PAWS: Paraphrase Adversaries from Word Scrambling**Yuan Zhang, Jason Baldridge, Luheng He*2019 · 584 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-paws-paraphrase-adversaries-word-scrambling/)
63. [**Minimum Bayes-Risk Decoding for Statistical Machine Translation**Shankar Kumar, William Byrne*2004 · 583 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2004-minimum-bayes-risk-decoding-statistical/)
64. [**Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer**Sudha Rao, Joel Tetreault*2018 · 581 citations*](https://md.research.elamur.ai/free/en/papers/rao-2018-dear-sir-or-madam-may/)
65. [**An Unsupervised Aspect-Sentiment Model for Online Reviews**Samuel Brody, Noemie Elhadad*2010 · 569 citations*](https://md.research.elamur.ai/free/en/papers/brody-2010-unsupervised-aspect-sentiment-model-online/)
66. [**Learning How to Ask: Querying LMs with Mixtures of Soft Prompts**Guanghui Qin, Jason Eisner*2021 · 566 citations*](https://md.research.elamur.ai/free/en/papers/qin-2021-learning-ask-querying-lms-mixtures/)
67. [**Sentence Level Discourse Parsing using Syntactic and Lexical Information**Radu Soricut, Daniel Marcu*2003 · 560 citations*](https://md.research.elamur.ai/free/en/papers/soricut-2003-sentence-level-discourse-parsing-syntactic/)
68. [**Unsupervised Modeling of Twitter Conversations**Alan Ritter, Colin Cherry, Bill Dolan*2010 · 557 citations*](https://md.research.elamur.ai/free/en/papers/ritter-2010-unsupervised-modeling-twitter-conversations/)
69. [**Batch Tuning Strategies for Statistical Machine Translation**Colin Cherry, George Foster*2012 · 548 citations*](https://md.research.elamur.ai/free/en/papers/cherry-2012-batch-tuning-strategies-statistical-machine/)
70. [**Learning to Compose Neural Networks for Question Answering**Jacob Andreas, Marcus Rohrbach, Trevor Darrell, et al.*2016 · 547 citations*](https://md.research.elamur.ai/free/en/papers/andreas-2016-learning-compose-neural-networks-question/)
71. [**End-to-End Open-Domain Question Answering with BERTserini**Wei Yang, Yuqing Xie, Aileen Lin, et al.*2019 · 539 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-end-end-open-domain-question/)
72. [**Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos**Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, et al.*2018 · 539 citations*](https://md.research.elamur.ai/free/en/papers/hazarika-2018-conversational-memory-network-emotion-recognition/)
73. [**Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation**Chao Xing, Dong Wang, Chao Liu, et al.*2015 · 528 citations*](https://md.research.elamur.ai/free/en/papers/xing-2015-normalized-word-embedding-orthogonal-transform/)
74. [**Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks**Ji Young Lee, Franck Dernoncourt*2016 · 509 citations*](https://md.research.elamur.ai/free/en/papers/lee-2016-sequential-short-text-classification-recurrent/)
75. [**Farasa: A Fast and Furious Segmenter for Arabic**Ahmed Abdelali, Kareem Darwish, Nadir Durrani, et al.*2016 · 506 citations*](https://md.research.elamur.ai/free/en/papers/abdelali-2016-farasa-fast-furious-segmenter-arabic/)
76. [**Two/Too Simple Adaptations of Word2Vec for Syntax Problems**Wang Ling, Chris Dyer, Alan W. Black, et al.*2015 · 504 citations*](https://md.research.elamur.ai/free/en/papers/ling-2015-two-too-simple-adaptations-word2vec/)
77. [**Gender Bias in Contextualized Word Embeddings**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2019 · 503 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2019-gender-bias-contextualized-word-embeddings/)
78. [**MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms**Aida Amini, Saadia Gabriel, Shanchuan Lin, et al.*2019 · 500 citations*](https://md.research.elamur.ai/free/en/papers/amini-2019-mathqa-interpretable-math-word-problem/)
79. [**Shallow Semantic Parsing using Support Vector Machines**Sameer S. Pradhan, Wayne H. Ward, Kadri Hacioglu, et al.*2004 · 497 citations*](https://md.research.elamur.ai/free/en/papers/pradhan-2004-shallow-semantic-parsing-support-vector/)
80. [**Learning Natural Language Inference with LSTM**Shuohang Wang, Jing Jiang*2016 · 491 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-learning-natural-language-inference-lstm/)
81. [**Multi-Prototype Vector-Space Models of Word Meaning**Joseph Reisinger, Raymond J. Mooney*2010 · 489 citations*](https://md.research.elamur.ai/free/en/papers/reisinger-2010-multi-prototype-vector-space-models/)
82. [**ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction**Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al.*2022 · 477 citations*](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/)
83. [**Learning Whom to Trust with MACE**Dirk Hovy, Taylor Berg-Kirkpatrick, Ashish Vaswani, et al.*2013 · 472 citations*](https://md.research.elamur.ai/free/en/papers/hovy-2013-learning-whom-trust-mace/)
84. [**What to do about bad language on the internet**Jacob Eisenstein*2013 · 469 citations*](https://md.research.elamur.ai/free/en/papers/eisenstein-2013-what-do-about-bad-language/)
85. [**Controlling Politeness in Neural Machine Translation via Side Constraints**Rico Sennrich, Barry Haddow, Alexandra Birch*2016 · 467 citations*](https://md.research.elamur.ai/free/en/papers/sennrich-2016-controlling-politeness-neural-machine-translation/)
86. [**When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?**Ye Qi, Devendra Sachan, Matthieu Felix, et al.*2018 · 466 citations*](https://md.research.elamur.ai/free/en/papers/qi-2018-when-why-are-pre-trained/)
87. [**Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval**Xiaodong Liu, Jianfeng Gao, Xiaodong He, et al.*2015 · 463 citations*](https://md.research.elamur.ai/free/en/papers/liu-2015-representation-learning-multi-task-deep/)
88. [**Emergent: a novel data-set for stance classification**William Ferreira, Andreas Vlachos*2016 · 456 citations*](https://md.research.elamur.ai/free/en/papers/ferreira-2016-emergent-novel-data-set-stance/)
89. [**Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System**Jost Schatzmann, Blaise Thomson, Karl Weilhammer, et al.*2007 · 452 citations*](https://md.research.elamur.ai/free/en/papers/schatzmann-2007-agenda-based-user-simulation-bootstrapping/)
90. [**Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation**Matt Post, David Vilar*2018 · 437 citations*](https://md.research.elamur.ai/free/en/papers/post-2018-fast-lexically-constrained-decoding-dynamic/)
91. [**MuST-C: a Multilingual Speech Translation Corpus**Mattia A. Di Gangi, Roldano Cattoni, Luisa Bentivogli, et al.*2019 · 436 citations*](https://md.research.elamur.ai/free/en/papers/gangi-2019-must-c-multilingual-speech-translation/)
92. [**Document-Level Event Argument Extraction by Conditional Generation**Sha Li, Heng Ji, Jiawei Han*2021 · 432 citations*](https://md.research.elamur.ai/free/en/papers/li-2021-document-level-event-argument-extraction/)
93. [**Self-Alignment Pretraining for Biomedical Entity Representations**Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng, et al.*2021 · 432 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-self-alignment-pretraining-biomedical-entity/)
94. [**Multi-Source Neural Translation**Barret Zoph, Kevin Knight*2016 · 432 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2016-multi-source-neural-translation/)
95. [**Deep Communicating Agents for Abstractive Summarization**Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, et al.*2018 · 431 citations*](https://md.research.elamur.ai/free/en/papers/celikyilmaz-2018-deep-communicating-agents-abstractive-summarization/)
96. [**A Unigram Orientation Model for Statistical Machine Translation**Christoph Tillmann*2004 · 430 citations*](https://md.research.elamur.ai/free/en/papers/tillmann-2004-unigram-orientation-model-statistical-machine/)
97. [**A general framework for information extraction using dynamic span graphs**Yi Luan, Dave Wadden, Luheng He, et al.*2019 · 427 citations*](https://md.research.elamur.ai/free/en/papers/luan-2019-general-framework-information-extraction-dynamic/)
98. [**Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics**Artidoro Pagnoni, Vidhisha Balachandran, Yulia Tsvetkov*2021 · 417 citations*](https://md.research.elamur.ai/free/en/papers/pagnoni-2021-understanding-factuality-abstractive-summarization-frank/)
99. [**Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them**Hila Gonen, Yoav Goldberg*2019 · 412 citations*](https://md.research.elamur.ai/free/en/papers/gonen-2019-lipstick-pig-debiasing-methods-cover/)
100. [**Factual Probing Is MASK: Learning vs. Learning to Recall**Zexuan Zhong, Dan Friedman, Danqi Chen*2021 · 411 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2021-factual-probing-is-mask-learning/)

**1** [2](https://md.research.elamur.ai/free/en/venues/naacl/works/page/2/) [3](https://md.research.elamur.ai/free/en/venues/naacl/works/page/3/) [4](https://md.research.elamur.ai/free/en/venues/naacl/works/page/4/) [next →](https://md.research.elamur.ai/free/en/venues/naacl/works/page/2/)

- [← back to](https://md.research.elamur.ai/free/en/venues/naacl/) — Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)