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# Works of United States

2809 · page 3 of 29 · the most cited first · [← United States](https://md.research.elamur.ai/free/en/countries/united-states/)

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201. [**Understanding Black-box Predictions via Influence Functions**Pang Wei Koh, Percy Liang*2017 · 1,478 citations*](https://md.research.elamur.ai/free/en/papers/koh-2017-understanding-black-box-predictions-influence/)
202. [**BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation**Junnan Li, Dongxu Li, Caiming Xiong, et al.*2022 · 1,463 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-blip-bootstrapping-language-image-pre/)
203. [**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/)
204. [**Sentiment Analysis of Twitter Data**Apoorv Agarwal, Boyi Xie, Ilia Vovsha, et al.*2011 · 1,457 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2011-sentiment-analysis-twitter-data/)
205. [**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/)
206. [**GLM: General Language Model Pretraining with Autoregressive Blank Infilling**Zhengxiao Du, Yujie Qian, Xiao Liu, et al.*2022 · 1,449 citations*](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/)
207. [**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/)
208. [**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/)
209. [**Sequence-Level Knowledge Distillation**Yoon Kim, Alexander M. Rush*2016 · 1,440 citations*](https://md.research.elamur.ai/free/en/papers/kim-2016-sequence-level-knowledge-distillation/)
210. [**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/)
211. [**Zero-Shot Text-to-Image Generation**Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, et al.*2021 · 1,423 citations*](https://md.research.elamur.ai/free/en/papers/ramesh-2021-zero-shot-text-image-generation/)
212. [**Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects**Jianmo Ni, Jiacheng Li, Julian McAuley*2019 · 1,421 citations*](https://md.research.elamur.ai/free/en/papers/ni-2019-justifying-recommendations-distantly-labeled-reviews/)
213. [**Language-agnostic BERT Sentence Embedding**Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, et al.*2020 · 1,412 citations*](https://md.research.elamur.ai/free/en/papers/feng-2020-language-agnostic-bert-sentence-embedding/)
214. [**Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions**Richard Socher, Jeffrey Pennington, Eric H. Huang, et al.*2011 · 1,411 citations*](https://md.research.elamur.ai/free/en/papers/socher-2011-semi-supervised-recursive-autoencoders-predicting/)
215. [**Contextual String Embeddings for Sequence Labeling**Alan Akbik, Duncan Blythe, Roland Vollgraf*2018 · 1,390 citations*](https://md.research.elamur.ai/free/en/papers/akbik-2018-contextual-string-embeddings-sequence-labeling/)
216. [**Unified Language Model Pre-training for Natural Language Understanding and Generation**Li Dong, Nan Yang, Wenhui Wang, et al.*2019 · 1,389 citations*](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/)
217. [**NLTK: The Natural Language Toolkit**Steven Bird*2006 · 1,372 citations*](https://md.research.elamur.ai/free/en/papers/bird-2006-nltk-natural-language-toolkit/)
218. [**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/)
219. [**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/)
220. [**Generation and Comprehension of Unambiguous Object Descriptions**Junhua Mao, Jonathan Huang, Alexander Toshev, et al.*2015 · 1,358 citations*](https://md.research.elamur.ai/free/en/papers/mao-2015-generation-comprehension-unambiguous-object-descriptions/)
221. [**Improving Word Representations via Global Context and Multiple Word Prototypes**Eric Huang, Richard Socher, Christopher Manning, et al.*2012 · 1,349 citations*](https://md.research.elamur.ai/free/en/papers/huang-2012-improving-word-representations-global-context/)
222. [**Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons**Andrew McCallum, Wei Li*2003 · 1,335 citations*](https://md.research.elamur.ai/free/en/papers/mccallum-2003-early-results-named-entity-recognition/)
223. [**Optimizing Semantic Coherence in Topic Models**David Mimno, Hanna Wallach, Edmund Talley, et al.*2011 · 1,334 citations*](https://md.research.elamur.ai/free/en/papers/mimno-2011-optimizing-semantic-coherence-topic-models/)
224. [**Sequence Level Training with Recurrent Neural Networks**Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, et al.*2015 · 1,325 citations*](https://md.research.elamur.ai/free/en/papers/ranzato-2015-sequence-level-training-recurrent-neural/)
225. [**Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment**Di Jin, Zhijing Jin, Joey Tianyi Zhou, et al.*2019 · 1,318 citations*](https://md.research.elamur.ai/free/en/papers/jin-2019-bert-really-robust-strong-baseline/)
226. [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing**Pengcheng He, Jianfeng Gao, Weizhu Chen*2021 · 1,316 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/)
227. [**Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking**Eugene Charniak, Mark Johnson*2005 · 1,312 citations*](https://md.research.elamur.ai/free/en/papers/charniak-2005-coarse-fine-n-best-parsing/)
228. [**Language Modeling with Gated Convolutional Networks**Yann N. Dauphin, Angela Fan, Michael Auli, et al.*2016 · 1,305 citations*](https://md.research.elamur.ai/free/en/papers/dauphin-2016-language-modeling-gated-convolutional-networks/)
229. [**VideoBERT: A Joint Model for Video and Language Representation Learning**Chen Sun, Austin Myers, Carl Vondrick, et al.*2019 · 1,305 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-videobert-joint-model-video-language/)
230. [**A Program for Aligning Sentences in Bilingual Corpora**William A. Gale, Kenneth W. Church*1993 · 1,284 citations*](https://md.research.elamur.ai/free/en/papers/gale-1993-program-aligning-sentences-bilingual-corpora/)
231. [**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/)
232. [**V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure**Andrew Rosenberg, Julia Hirschberg*2007 · 1,279 citations*](https://md.research.elamur.ai/free/en/papers/rosenberg-2007-vmeasure-conditional-entropy-cluster-evaluation/)
233. [**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/)
234. [**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/)
235. [**ReferItGame: Referring to Objects in Photographs of Natural Scenes**Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, et al.*2014 · 1,276 citations*](https://md.research.elamur.ai/free/en/papers/kazemzadeh-2014-referitgame-referring-objects-photographs-natural/)
236. [**Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change**William L. Hamilton, Jure Leskovec, Dan Jurafsky*2016 · 1,270 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2016-diachronic-word-embeddings-reveal-statistical/)
237. [**Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts**Cícero dos Santos, Maíra Gatti*2014 · 1,269 citations*](https://md.research.elamur.ai/free/en/papers/santos-2014-deep-convolutional-neural-networks-sentiment/)
238. [**CamemBERT: a Tasty French Language Model**Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, et al.*2019 · 1,262 citations*](https://md.research.elamur.ai/free/en/papers/martin-2019-camembert-tasty-french-language-model/)
239. [**Non-Projective Dependency Parsing using Spanning Tree Algorithms**Ryan McDonald, Fernando Pereira, Kiril Ribarov, et al.*2005 · 1,258 citations*](https://md.research.elamur.ai/free/en/papers/mcdonald-2005-non-projective-dependency-parsing-spanning/)
240. [**Learning Accurate, Compact, and Interpretable Tree Annotation**Slav Petrov, Leon Barrett, Romain Thibaux, et al.*2006 · 1,242 citations*](https://md.research.elamur.ai/free/en/papers/petrov-2006-learning-accurate-compact-interpretable-tree/)
241. [**What Makes Good In-Context Examples for GPT-3?**Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al.*2022 · 1,235 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/)
242. [**Latent Retrieval for Weakly Supervised Open Domain Question Answering**Kenton Lee, Ming-Wei Chang, Kristina Toutanova*2019 · 1,234 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-latent-retrieval-weakly-supervised-open/)
243. [**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/)
244. [**Learning Extraction Patterns for Subjective Expressions**Ellen Riloff, Janyce Wiebe*2003 · 1,217 citations*](https://md.research.elamur.ai/free/en/papers/riloff-2003-learning-extraction-patterns-subjective-expressions/)
245. [**Online Large-Margin Training of Dependency Parsers**Ryan McDonald, Koby Crammer, Fernando Pereira*2005 · 1,212 citations*](https://md.research.elamur.ai/free/en/papers/mcdonald-2005-online-large-margin-training-dependency/)
246. [**Publicly Available Clinical BERT Embeddings**Emily Alsentzer, John R. Murphy, Willie Boag, et al.*2019 · 1,209 citations*](https://md.research.elamur.ai/free/en/papers/alsentzer-2019-publicly-available-clinical-bert-embeddings/)
247. [**An Empirical Study of Smoothing Techniques for Language Modeling**Stanley F. Chen, Joshua Goodman*1996 · 1,209 citations*](https://md.research.elamur.ai/free/en/papers/chen-1996-empirical-study-smoothing-techniques-language/)
248. [**Recipes for Building an Open-Domain Chatbot**Stephen Roller, Emily Dinan, Naman Goyal, et al.*2021 · 1,206 citations*](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/)
249. [**Opinion Word Expansion and Target Extraction through Double Propagation**Guang Qiu, Bing Liu, Jiajun Bu, et al.*2011 · 1,196 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2011-opinion-word-expansion-target-extraction/)
250. [**Attention is not not Explanation**Sarah Wiegreffe, Yuval Pinter*2019 · 1,192 citations*](https://md.research.elamur.ai/free/en/papers/wiegreffe-2019-attention-is-not-not-explanation/)
251. [**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/)
252. [**A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly**Yifan Yao, Jinhao Duan, Kaidi Xu, et al.*2023 · 1,181 citations*](https://md.research.elamur.ai/free/en/papers/yao-2023-survey-large-language-model-llm/)
253. [**Efficient Low-rank Multimodal Fusion With Modality-Specific Factors**Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, et al.*2018 · 1,174 citations*](https://md.research.elamur.ai/free/en/papers/liu-2018-efficient-low-rank-multimodal-fusion/)
254. [**A Shortest Path Dependency Kernel for Relation Extraction**Razvan Bunescu, Raymond Mooney*2005 · 1,173 citations*](https://md.research.elamur.ai/free/en/papers/bunescu-2005-shortest-path-dependency-kernel-relation/)
255. [**Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering**Yash Goyal, Tejas Khot, Douglas Summers-Stay, et al.*2016 · 1,169 citations*](https://md.research.elamur.ai/free/en/papers/goyal-2016-making-v-vqa-matter-elevating/)
256. [**On the Cross-lingual Transferability of Monolingual Representations**Mikel Artetxe, Sebastian Ruder, Dani Yogatama*2020 · 1,168 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2020-cross-lingual-transferability-monolingual-representations/)
257. [**Modeling Context in Referring Expressions**Licheng Yu, Patrick Poirson, Shan Yang, et al.*2016 · 1,165 citations*](https://md.research.elamur.ai/free/en/papers/yu-2016-modeling-context-referring-expressions/)
258. [**Distributional Clustering of English Words**Fernando Pereira, Naftali Tishby, Lillian Lee*1993 · 1,160 citations*](https://md.research.elamur.ai/free/en/papers/pereira-1993-distributional-clustering-english-words/)
259. [**SemEval-2017 Task 4: Sentiment Analysis in Twitter**Sara Rosenthal, Noura Farra, Preslav Nakov*2017 · 1,159 citations*](https://md.research.elamur.ai/free/en/papers/rosenthal-2017-semeval-2017-task-4-sentiment/)
260. [**Generative Modeling by Estimating Gradients of the Data Distribution**Yang Song, Stefano Ermon*2019 · 1,155 citations*](https://md.research.elamur.ai/free/en/papers/song-2019-generative-modeling-estimating-gradients-data/)
261. [**PubMedQA: A Dataset for Biomedical Research Question Answering**Qiao Jin, Bhuwan Dhingra, Zhengping Liu, et al.*2019 · 1,152 citations*](https://md.research.elamur.ai/free/en/papers/jin-2019-pubmedqa-dataset-biomedical-research-question/)
262. [**"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/)
263. [**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/)
264. [**The Stanford Typed Dependencies Representation**Marie-Catherine de Marneffe, Christopher D. Manning*2008 · 1,144 citations*](https://md.research.elamur.ai/free/en/papers/marneffe-2008-stanford-typed-dependencies-representation/)
265. [**Least-to-most prompting enables complex reasoning in large language models***2022 · 1,143 citations*](https://md.research.elamur.ai/free/en/papers/al-2022-least-most-prompting-enables-complex/)
266. [**BERTweet: A pre-trained language model for English Tweets**Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen*2020 · 1,139 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2020-bertweet-pre-trained-language-model/)
267. [**StereoSet: Measuring stereotypical bias in pretrained language models**Moin Nadeem, Anna Bethke, Siva Reddy*2021 · 1,131 citations*](https://md.research.elamur.ai/free/en/papers/nadeem-2021-stereoset-measuring-stereotypical-bias-pretrained/)
268. [**Autonomous chemical research with large language models**Daniil A. Boiko, Robert MacKnight, Ben Kline, et al.*2023 · 1,122 citations*](https://md.research.elamur.ai/free/en/papers/boiko-2023-autonomous-chemical-research-large-language/)
269. [**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/)
270. [**WikiQA: A Challenge Dataset for Open-Domain Question Answering**Yi Yang, Wen-tau Yih, Christopher Meek*2015 · 1,117 citations*](https://md.research.elamur.ai/free/en/papers/yang-2015-wikiqa-challenge-dataset-open-domain/)
271. [**Baselines and Bigrams: Simple, Good Sentiment and Topic Classification**Sida Wang, Christopher Manning*2012 · 1,116 citations*](https://md.research.elamur.ai/free/en/papers/wang-2012-baselines-bigrams-simple-good-sentiment/)
272. [**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/)
273. [**Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm**Laria Reynolds, Kyle McDonell*2021 · 1,111 citations*](https://md.research.elamur.ai/free/en/papers/reynolds-2021-prompt-programming-large-language-models/)
274. [**Very Deep Convolutional Networks for Text Classification**Alexis Conneau, Holger Schwenk, Loïc Barrault, et al.*2017 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2017-very-deep-convolutional-networks-text/)
275. [**SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2019 · 1,109 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2019-semeval-2019-task-6-identifying/)
276. [**SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity**Eneko Agirre, Daniel Cer, Mona Diab, et al.*2012 · 1,107 citations*](https://md.research.elamur.ai/free/en/papers/agirre-2012-semeval-2012-task-6-pilot/)
277. [**Generating Natural Language Adversarial Examples**Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, et al.*2018 · 1,102 citations*](https://md.research.elamur.ai/free/en/papers/alzantot-2018-generating-natural-language-adversarial-examples/)
278. [**Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them**Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al.*2023 · 1,094 citations*](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/)
279. [**SemEval-2018 Task 1: Affect in Tweets**Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, et al.*2018 · 1,092 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2018-semeval-2018-task-1-affect/)
280. [**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/)
281. [**Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger**Kristina Toutanova, Christopher D. Manning*2000 · 1,082 citations*](https://md.research.elamur.ai/free/en/papers/toutanova-2000-enriching-knowledge-sources-used-maximum/)
282. [**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/)
283. [**Neural Module Networks**Jacob Andreas, Marcus Rohrbach, Trevor Darrell, et al.*2015 · 1,075 citations*](https://md.research.elamur.ai/free/en/papers/andreas-2015-neural-module-networks/)
284. [**Bias and Fairness in Large Language Models: A Survey**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,072 citations*](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/)
285. [**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/)
286. [**Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets**Yifan Peng, Shankai Yan, Zhiyong Lu*2019 · 1,070 citations*](https://md.research.elamur.ai/free/en/papers/peng-2019-transfer-learning-biomedical-natural-language/)
287. [**CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes**Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, et al.*2012 · 1,064 citations*](https://md.research.elamur.ai/free/en/papers/pradhan-2012-conll-2012-shared-task-modeling/)
288. [**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/)
289. [**The Penn Treebank: Annotating Predicate Argument Structure**Mitchell Marcus, Grace Kim, Mary Ann Marcinkiewicz, et al.*1994 · 1,056 citations*](https://md.research.elamur.ai/free/en/papers/marcus-1994-penn-treebank-annotating-predicate-argument/)
290. [**A Model-Theoretic Coreference Scoring Scheme**Marc Vilain, John Burger, John Aberdeen, et al.*1995 · 1,055 citations*](https://md.research.elamur.ai/free/en/papers/vilain-1995-model-theoretic-coreference-scoring-scheme/)
291. [**InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning**Wenliang Dai, Junnan Li, Dongxu Li, et al.*2023 · 1,051 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-instructblip-towards-general-purpose-vision/)
292. [**ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs**Wenpeng Yin, Hinrich Schütze, Bing Xiang, et al.*2016 · 1,048 citations*](https://md.research.elamur.ai/free/en/papers/yin-2016-abcnn-attention-based-convolutional-neural/)
293. [**Graph Convolution over Pruned Dependency Trees Improves Relation Extraction**Yuhao Zhang, Peng Qi, Christopher D. Manning*2018 · 1,046 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2018-graph-convolution-over-pruned-dependency/)
294. [**Three Generative, Lexicalised Models for Statistical Parsing**Michael Collins*1997 · 1,045 citations*](https://md.research.elamur.ai/free/en/papers/collins-1997-three-generative-lexicalised-models-statistical/)
295. [**Adversarial Learning for Neural Dialogue Generation**Jiwei Li, Will Monroe, Tianlin Shi, et al.*2017 · 1,044 citations*](https://md.research.elamur.ai/free/en/papers/li-2017-adversarial-learning-neural-dialogue-generation/)
296. [**GPT Understands, Too**Xiao Liu, Yanan Zheng, Zhengxiao Du, et al.*2021 · 1,039 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-gpt-understands-too/)
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