---
title: "University of Washington — works"
description: "206 works by researchers of University of Washington in our library · page 1 of 3, the most cited first."
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# Works of University of Washington

206 works · page 1 of 3 · the most cited first · [← University of Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/)

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1. [**SciPy 1.0 — Fundamental Algorithms for Scientific Computing in Python**Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, et al.*2019 · 38,317 citations*](https://md.research.elamur.ai/free/en/papers/virtanen-2019-scipy-1-0-fundamental-algorithms/)
2. [**BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,752 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/)
3. [**A Unified Approach to Interpreting Model Predictions**Scott Lundberg, Su-In Lee*2017 · 8,086 citations*](https://md.research.elamur.ai/free/en/papers/lundberg-2017-unified-approach-interpreting-model-predictions/)
4. [**GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,141 citations*](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/)
5. [**Dense Passage Retrieval for Open-Domain Question Answering**Vladimir Karpukhin, Barlas Oguz, Sewon Min, et al.*2020 · 5,370 citations*](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/)
6. [**TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension**Mandar Joshi, Eunsol Choi, Daniel Weld, et al.*2017 · 3,204 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/)
7. [**SpanBERT: Improving Pre-training by Representing and Predicting Spans**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,580 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/)
8. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,187 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
9. [**Extracting Product Features and Opinions from Reviews**Ana-Maria Popescu, Oren Etzioni*2005 · 1,904 citations*](https://md.research.elamur.ai/free/en/papers/popescu-2005-extracting-product-features-opinions-reviews/)
10. [**Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks**Xiujun Li, Xi Yin, Chunyuan Li, et al.*2020 · 1,827 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-oscar-object-semantics-aligned-pre/)
11. [**CLIPScore: A Reference-free Evaluation Metric for Image Captioning**Jack Hessel, Ari Holtzman, Maxwell Forbes, et al.*2021 · 1,498 citations*](https://md.research.elamur.ai/free/en/papers/hessel-2021-clipscore-reference-free-evaluation-metric/)
12. [**Open Information Extraction from the Web**Michele Banko, Michael J. Cafarella, Stephen Soderland, et al.*2007 · 1,459 citations*](https://md.research.elamur.ai/free/en/papers/banko-2007-open-information-extraction-web/)
13. [**Named Entity Recognition in Tweets: An Experimental Study**Alan Ritter, Sam Clark, Mausam, et al.*2011 · 1,451 citations*](https://md.research.elamur.ai/free/en/papers/ritter-2011-named-entity-recognition-tweets-experimental/)
14. [**Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech**Andreas Stolcke, Klaus Ries, Noah Coccaro, et al.*2000 · 1,447 citations*](https://md.research.elamur.ai/free/en/papers/stolcke-2000-dialogue-act-modeling-automatic-tagging/)
15. [**Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?**Sewon Min, Xinxi Lyu, Ari Holtzman, et al.*2022 · 1,423 citations*](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/)
16. [**Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data**Emily M. Bender, Alexander Koller*2020 · 1,371 citations*](https://md.research.elamur.ai/free/en/papers/bender-2020-climbing-nlu-meaning-form-understanding/)
17. [**TVM: An Automated End-to-End Optimizing Compiler for Deep Learning**Tianqi Chen, Thierry Moreau, Ziheng Jiang, et al.*2018 · 1,371 citations*](https://md.research.elamur.ai/free/en/papers/chen-2018-tvm-automated-end-end-optimizing/)
18. [**COMET: Commonsense Transformers for Automatic Knowledge Graph Construction**Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al.*2019 · 1,282 citations*](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/)
19. [**RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,277 citations*](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/)
20. [**End-to-end Neural Coreference Resolution**Kenton Lee, Luheng He, Mike Lewis, et al.*2017 · 1,277 citations*](https://md.research.elamur.ai/free/en/papers/lee-2017-end-end-neural-coreference-resolution/)
21. [**PIQA: Reasoning about Physical Commonsense in Natural Language**Yonatan Bisk, Rowan Zellers, Ronan Le Bras, et al.*2019 · 1,218 citations*](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/)
22. [**"Why Should I Trust You?": Explaining the Predictions of Any Classifier**Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin*2016 · 1,148 citations*](https://md.research.elamur.ai/free/en/papers/ribeiro-2016-why-should-i-trust-explaining/)
23. [**The Risk of Racial Bias in Hate Speech Detection**Maarten Sap, Dallas Card, Saadia Gabriel, et al.*2019 · 1,148 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-risk-racial-bias-hate-speech/)
24. [**SemEval-2016 Task 4: Sentiment Analysis in Twitter**Preslav Nakov, Alan Ritter, Sara Rosenthal, et al.*2016 · 1,081 citations*](https://md.research.elamur.ai/free/en/papers/nakov-2016-semeval-2016-task-4-sentiment/)
25. [**Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking**Hannah Rashkin, Eunsol Choi, Jin Yea Jang, et al.*2017 · 1,072 citations*](https://md.research.elamur.ai/free/en/papers/rashkin-2017-truth-varying-shades-analyzing-language/)
26. [**QuAC: Question Answering in Context**Eunsol Choi, He He, Mohit Iyyer, et al.*2018 · 1,057 citations*](https://md.research.elamur.ai/free/en/papers/choi-2018-quac-question-answering-context/)
27. [**Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2017 · 1,030 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2017-men-also-like-shopping-reducing/)
28. [**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/)
29. [**FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation**Sewon Min, Kalpesh Krishna, Xinxi Lyu, et al.*2023 · 970 citations*](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/)
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. [**Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction**Yi Luan, Luheng He, Mari Ostendorf, et al.*2018 · 933 citations*](https://md.research.elamur.ai/free/en/papers/luan-2018-multi-task-identification-entities-relations/)
32. [**Using the Output Embedding to Improve Language Models**Ofir Press, Lior Wolf*2017 · 891 citations*](https://md.research.elamur.ai/free/en/papers/press-2017-output-embedding-improve-language-models/)
33. [**Is Attention Interpretable?**Sofia Serrano, Noah A. Smith*2019 · 885 citations*](https://md.research.elamur.ai/free/en/papers/serrano-2019-is-attention-interpretable/)
34. [**UNIFIEDQA: Crossing Format Boundaries with a Single QA System**Daniel Khashabi, Sewon Min, Tushar Khot, et al.*2020 · 843 citations*](https://md.research.elamur.ai/free/en/papers/khashabi-2020-unifiedqa-crossing-format-boundaries-single/)
35. [**Entity, Relation, and Event Extraction with Contextualized Span Representations**David Wadden, Ulme Wennberg, Yi Luan, et al.*2019 · 818 citations*](https://md.research.elamur.ai/free/en/papers/wadden-2019-entity-relation-event-extraction-contextualized/)
36. [**Domain Adaptation via Pseudo In-Domain Data Selection**Amittai Axelrod, Xiaodong He, Jianfeng Gao*2011 · 773 citations*](https://md.research.elamur.ai/free/en/papers/axelrod-2011-domain-adaptation-pseudo-domain-data/)
37. [**Reproducible scaling laws for contrastive language-image learning**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 750 citations*](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/)
38. [**ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks**Mohit Shridhar; Jesse Thomason; Daniel Gordon; Yonatan Bisk; Winson Han; Roozbeh Mottaghi; Luke Zettlemoyer;, Dieter Fox*2019 · 724 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2019-alfred-benchmark-interpreting-grounded-instructions/)
39. [**Fact or Fiction: Verifying Scientific Claims**David Wadden, Shanchuan Lin, Kyle Lo, et al.*2020 · 706 citations*](https://md.research.elamur.ai/free/en/papers/wadden-2020-fact-or-fiction-verifying-scientific/)
40. [**Data-Driven Response Generation in Social Media**Alan Ritter, Colin Cherry, William B. Dolan*2011 · 692 citations*](https://md.research.elamur.ai/free/en/papers/ritter-2011-data-driven-response-generation-social/)
41. [**SPECTER: Document-level Representation Learning using Citation-informed Transformers**Arman Cohan, Sergey Feldman, Iz Beltagy, et al.*2020 · 690 citations*](https://md.research.elamur.ai/free/en/papers/cohan-2020-specter-document-level-representation-learning/)
42. [**CORD-19: The COVID-19 Open Research Dataset**Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, et al.*2020 · 685 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-cord-19-covid-19-open/)
43. [**Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes**Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al.*2023 · 684 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/)
44. [**A Class of Submodular Functions for Document Summarization**Hui Lin, Jeff Bilmes*2011 · 683 citations*](https://md.research.elamur.ai/free/en/papers/lin-2011-class-submodular-functions-document-summarization/)
45. [**Social Bias Frames: Reasoning about Social and Power Implications of Language**Maarten Sap, Saadia Gabriel, Lianhui Qin, et al.*2020 · 680 citations*](https://md.research.elamur.ai/free/en/papers/sap-2020-social-bias-frames-reasoning-about/)
46. [**Open Information Extraction Using Wikipedia**Fei Wu, Daniel S. Weld*2010 · 648 citations*](https://md.research.elamur.ai/free/en/papers/wu-2010-open-information-extraction-wikipedia/)
47. [**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/)
48. [**Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions**Yoav Artzi, Luke Zettlemoyer*2013 · 592 citations*](https://md.research.elamur.ai/free/en/papers/artzi-2013-weakly-supervised-learning-semantic-parsers/)
49. [**Deep Semantic Role Labeling: What Works and What's Next**Luheng He, Kenton Lee, Mike Lewis, et al.*2017 · 589 citations*](https://md.research.elamur.ai/free/en/papers/he-2017-deep-semantic-role-labeling-what/)
50. [**Evaluating Gender Bias in Machine Translation**Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer*2019 · 588 citations*](https://md.research.elamur.ai/free/en/papers/stanovsky-2019-evaluating-gender-bias-machine-translation/)
51. [**Dissecting Contextual Word Embeddings: Architecture and Representation**Matthew E. Peters, Mark Neumann, Luke Zettlemoyer, et al.*2018 · 585 citations*](https://md.research.elamur.ai/free/en/papers/peters-2018-dissecting-contextual-word-embeddings-architecture/)
52. [**s1: Simple test-time scaling**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 584 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/)
53. [**AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation**Qingyun Wu, Gagan Bansal, Jieyu Zhang, et al.*2023 · 584 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-autogen-enabling-next-gen-llm/)
54. [**ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection**Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, et al.*2022 · 571 citations*](https://md.research.elamur.ai/free/en/papers/hartvigsen-2022-toxigen-large-scale-machine-generated/)
55. [**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/)
56. [**Simple and Effective Multi-Paragraph Reading Comprehension**Christopher Clark, Matt Gardner*2017 · 556 citations*](https://md.research.elamur.ai/free/en/papers/clark-2017-simple-effective-multi-paragraph-reading/)
57. [**Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning**Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, et al.*2019 · 536 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/)
58. [**Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning**Armen Aghajanyan, Sonal Gupta, Luke Zettlemoyer*2021 · 530 citations*](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-intrinsic-dimensionality-explains-effectiveness-language/)
59. [**Instruction Tuning for Large Language Models: A Survey**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 515 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/)
60. [**Learning to Solve Arithmetic Word Problems with Verb Categorization**Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, et al.*2014 · 513 citations*](https://md.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/)
61. [**Crowdsourcing Multiple Choice Science Questions**Johannes Welbl, Nelson F. Liu, Matt Gardner*2017 · 513 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2017-crowdsourcing-multiple-choice-science-questions/)
62. [**BERT for Coreference Resolution: Baselines and Analysis**Mandar Joshi, Omer Levy, Luke Zettlemoyer, et al.*2019 · 488 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2019-bert-coreference-resolution-baselines-analysis/)
63. [**REPLUG: Retrieval-Augmented Black-Box Language Models**Weijia Shi, Sewon Min, Michihiro Yasunaga, et al.*2023 · 482 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/)
64. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 474 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
65. [**Defending Against Neural Fake News**Rowan Zellers, Ari Holtzman, Hannah Rashkin, et al.*2019 · 461 citations*](https://md.research.elamur.ai/free/en/papers/zellers-2019-defending-against-neural-fake-news/)
66. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 459 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/)
67. [**Reading Level Assessment Using Support Vector Machines and Statistical Language Models**Sarah Schwarm, Mari Ostendorf*2005 · 447 citations*](https://md.research.elamur.ai/free/en/papers/schwarm-2005-reading-level-assessment-support-vector/)
68. [**Paraphrase-Driven Learning for Open Question Answering**Anthony Fader, Luke Zettlemoyer, Oren Etzioni*2013 · 438 citations*](https://md.research.elamur.ai/free/en/papers/fader-2013-paraphrase-driven-learning-open-question/)
69. [**GPT-NER: Named Entity Recognition via Large Language Models**Shuhe Wang, Xiaofei Sun, Xiaoya Li, et al.*2025 · 436 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-gpt-ner-named-entity-recognition/)
70. [**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/)
71. [**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/)
72. [**Neural AMR: Sequence-to-Sequence Models for Parsing and Generation**Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, et al.*2017 · 425 citations*](https://md.research.elamur.ai/free/en/papers/konstas-2017-neural-amr-sequence-sequence-models/)
73. [**Gender and Dialect Bias in YouTube's Automatic Captions**Rachael Tatman*2017 · 413 citations*](https://md.research.elamur.ai/free/en/papers/tatman-2017-gender-dialect-bias-youtube-s/)
74. [**A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support**Ashish Sharma, Adam Miner, David Atkins, et al.*2020 · 408 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2020-computational-approach-understanding-empathy-expressed/)
75. [**UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models**Tianbao Xie, Chen Henry Wu, Peng Shi, et al.*2022 · 405 citations*](https://md.research.elamur.ai/free/en/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/)
76. [**Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs**Miguel Ballesteros, Chris Dyer, Noah A. Smith*2015 · 401 citations*](https://md.research.elamur.ai/free/en/papers/ballesteros-2015-improved-transition-based-parsing-modeling/)
77. [**DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts**Alisa Liu, Maarten Sap, Ximing Lu, et al.*2021 · 399 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-dexperts-decoding-time-controlled-text/)
78. [**Many Languages, One Parser**Waleed Ammar, George Mulcaire, Miguel Ballesteros, et al.*2016 · 382 citations*](https://md.research.elamur.ai/free/en/papers/ammar-2016-many-languages-one-parser/)
79. [**Tulu 3: Pushing Frontiers in Open Language Model Post-Training**Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al.*2024 · 379 citations*](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/)
80. [**MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension**Adam Fisch, Alon Talmor, Robin Jia, et al.*2019 · 375 citations*](https://md.research.elamur.ai/free/en/papers/fisch-2019-mrqa-2019-shared-task-evaluating/)
81. [**Editing Models with Task Arithmetic**Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al.*2022 · 374 citations*](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/)
82. [**A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers**Pradeep Dasigi, Kyle Lo, Iz Beltagy, et al.*2021 · 372 citations*](https://md.research.elamur.ai/free/en/papers/dasigi-2021-dataset-information-seeking-questions-answers/)
83. [**Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection**Maarten Sap, Swabha Swayamdipta, Laura Vianna, et al.*2022 · 364 citations*](https://md.research.elamur.ai/free/en/papers/sap-2022-annotators-attitudes-how-annotator-beliefs/)
84. [**Unsupervised Semantic Parsing**Hoifung Poon, Pedro Domingos*2009 · 345 citations*](https://md.research.elamur.ai/free/en/papers/poon-2009-unsupervised-semantic-parsing/)
85. [**Symbolic Knowledge Distillation: from General Language Models to Commonsense Models**Peter West, Chandra Bhagavatula, Jack Hessel, et al.*2022 · 343 citations*](https://md.research.elamur.ai/free/en/papers/west-2022-symbolic-knowledge-distillation-general-language/)
86. [**DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension**Kai Sun, Dian Yu, Jianshu Chen, et al.*2019 · 331 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-dream-challenge-data-set-models/)
87. [**Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language**Luheng He, Mike Lewis, Luke Zettlemoyer*2015 · 322 citations*](https://md.research.elamur.ai/free/en/papers/he-2015-question-answer-driven-semantic-role/)
88. [**Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data**Leon Derczynski, Alan Ritter, Sam Clark, et al.*2013 · 316 citations*](https://md.research.elamur.ai/free/en/papers/derczynski-2013-twitter-part-speech-tagging-all/)
89. [**WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation**Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.*2022 · 312 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/)
90. [**Multi-hop Reading Comprehension through Question Decomposition and Rescoring**Sewon Min, Victor Zhong, Luke Zettlemoyer, et al.*2019 · 306 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-multi-hop-reading-comprehension-through/)
91. [**From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models**Shangbin Feng, Chan Young Park, Yuhan Liu, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/feng-2023-pretraining-data-language-models-downstream/)
92. [**Abductive Commonsense Reasoning**Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, et al.*2019 · 301 citations*](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/)
93. [**Learning to Write with Cooperative Discriminators**Ari Holtzman, Jan Buys, Maxwell Forbes, et al.*2018 · 301 citations*](https://md.research.elamur.ai/free/en/papers/holtzman-2018-learning-write-cooperative-discriminators/)
94. [**Globally Coherent Text Generation with Neural Checklist Models**Chloé Kiddon, Luke Zettlemoyer, Yejin Choi*2016 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kiddon-2016-globally-coherent-text-generation-neural/)
95. [**ALFWorld: Aligning Text and Embodied Environments for Interactive Learning**Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, et al.*2020 · 299 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2020-alfworld-aligning-text-embodied-environments/)
96. [**Lexical Generalization in CCG Grammar Induction for Semantic Parsing**Tom Kwiatkowski, Luke Zettlemoyer, Sharon Goldwater, et al.*2011 · 283 citations*](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2011-lexical-generalization-ccg-grammar-induction/)
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