---
title: "Johns Hopkins University — works"
description: "151 works by researchers of Johns Hopkins University in our library · page 1 of 2, the most cited first."
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# Works of Johns Hopkins University

151 works · page 1 of 2 · the most cited first · [← Johns Hopkins University](https://md.research.elamur.ai/free/en/universities/johns-hopkins-university/)

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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. [**Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging**Eric Brill*1995 · 1,774 citations*](https://md.research.elamur.ai/free/en/papers/brill-1995-transformation-based-error-driven-learning/)
3. [**Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference**R. Thomas McCoy, Ellie Pavlick, Tal Linzen*2019 · 1,630 citations*](https://md.research.elamur.ai/free/en/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/)
4. [**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/)
5. [**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/)
6. [**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/)
7. [**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/)
8. [**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/)
9. [**Optimizing Statistical Machine Translation for Text Simplification**Wei Xu, Courtney Napoles, Ellie Pavlick, et al.*2016 · 952 citations*](https://md.research.elamur.ai/free/en/papers/xu-2016-optimizing-statistical-machine-translation-text/)
10. [**Findings of the 2016 Conference on Machine Translation**Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al.*2016 · 936 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2016-findings-2016-conference-machine-translation/)
11. [**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/)
12. [**Findings of the 2019 Conference on Machine Translation (WMT19)**Loïc Barrault, Ondřej Bojar, Marta R. Costa-jussà, et al.*2019 · 839 citations*](https://md.research.elamur.ai/free/en/papers/barrault-2019-findings-2019-conference-machine-translation/)
13. [**Quantifying Mental Health Signals in Twitter**Glen Coppersmith, Mark Dredze, Craig Harman*2014 · 821 citations*](https://md.research.elamur.ai/free/en/papers/coppersmith-2014-quantifying-mental-health-signals-twitter/)
14. [**Explainability for Large Language Models: A Survey**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 785 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/)
15. [**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/)
16. [**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/)
17. [**SemEval-2014 Task 9: Sentiment Analysis in Twitter**Sara Rosenthal, Alan Ritter, Preslav Nakov, et al.*2014 · 741 citations*](https://md.research.elamur.ai/free/en/papers/rosenthal-2014-semeval-2014-task-9-sentiment/)
18. [**Inducing Multilingual Text Analysis Tools via Robust Projection across Aligned Corpora**David Yarowsky, Grace Ngai, Richard Wicentowski*2001 · 734 citations*](https://md.research.elamur.ai/free/en/papers/yarowsky-2001-inducing-multilingual-text-analysis-tools/)
19. [**The Web as a Parallel Corpus**Philip Resnik, Noah A. Smith*2003 · 703 citations*](https://md.research.elamur.ai/free/en/papers/resnik-2003-web-as-parallel-corpus/)
20. [**Findings of the 2017 Conference on Machine Translation (WMT17)**Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al.*2017 · 685 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2017-findings-2017-conference-machine-translation/)
21. [**Problems in Current Text Simplification Research: New Data Can Help**Wei Xu, Chris Callison-Burch, Courtney Napoles*2015 · 659 citations*](https://md.research.elamur.ai/free/en/papers/xu-2015-problems-current-text-simplification-research/)
22. [**BloombergGPT: A Large Language Model for Finance**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
23. [**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/)
24. [**What do you learn from context? Probing for sentence structure in contextualized word representations**Ian Tenney, Patrick Xia, Berlin Chen, et al.*2019 · 582 citations*](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/)
25. [**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/)
26. [**Cross-Sentence N-ary Relation Extraction with Graph LSTMs**Nanyun Peng, Hoifung Poon, Chris Quirk, et al.*2017 · 553 citations*](https://md.research.elamur.ai/free/en/papers/peng-2017-cross-sentence-n-ary-relation/)
27. [**Edinburgh System Description for the 2005 IWSLT Speech Translation Evaluation**Philipp Koehn, Amittai Axelrod, Alexandra Birch Mayne, et al.*2005 · 545 citations*](https://md.research.elamur.ai/free/en/papers/koehn-2005-edinburgh-system-description-2005-iwslt/)
28. [**Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings**Nanyun Peng, Mark Dredze*2015 · 481 citations*](https://md.research.elamur.ai/free/en/papers/peng-2015-named-entity-recognition-chinese-social/)
29. [**Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT**Shijie Wu, Mark Dredze*2019 · 466 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-beto-bentz-becas-surprising-cross/)
30. [**Are All Languages Created Equal in Multilingual BERT?**Shijie Wu, Mark Dredze*2020 · 458 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-are-all-languages-created-equal/)
31. [**Contrastive Estimation: Training Log-Linear Models on Unlabeled Data**Noah A. Smith, Jason Eisner*2005 · 445 citations*](https://md.research.elamur.ai/free/en/papers/smith-2005-contrastive-estimation-training-log-linear/)
32. [**Findings of the 2013 Workshop on Statistical Machine Translation**Ondřej Bojar, Christian Buck, Chris Callison-Burch, et al.*2013 · 441 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2013-findings-2013-workshop-statistical-machine/)
33. [**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/)
34. [**WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia**Holger Schwenk, Vishrav Chaudhary, Shuo Sun, et al.*2019 · 424 citations*](https://md.research.elamur.ai/free/en/papers/schwenk-2019-wikimatrix-mining-135m-parallel-sentences/)
35. [**Iterative Back-Translation for Neural Machine Translation**Vu Cong Duy Hoang, Philipp Koehn, Gholamreza Haffari, et al.*2018 · 420 citations*](https://md.research.elamur.ai/free/en/papers/hoang-2018-iterative-back-translation-neural-machine/)
36. [**PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification**Ellie Pavlick, Pushpendre Rastogi, Juri Ganitkevitch, et al.*2015 · 414 citations*](https://md.research.elamur.ai/free/en/papers/pavlick-2015-ppdb-2-0-better-paraphrase/)
37. [**CLPsych 2015 Shared Task: Depression and PTSD on Twitter**Glen Coppersmith, Mark Dredze, Craig Harman, et al.*2015 · 410 citations*](https://md.research.elamur.ai/free/en/papers/coppersmith-2015-clpsych-2015-shared-task-depression/)
38. [**Findings of the 2020 Conference on Machine Translation (WMT20)**Loïc Barrault, Magdalena Biesialska, Ondřej Bojar, et al.*2020 · 401 citations*](https://md.research.elamur.ai/free/en/papers/barrault-2020-findings-2020-conference-machine-translation/)
39. [**Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning**Mitchell Gordon, Kevin Duh, Nicholas Andrews*2020 · 400 citations*](https://md.research.elamur.ai/free/en/papers/gordon-2020-compressing-bert-studying-effects-weight/)
40. [**MEAD - A Platform for Multidocument Multilingual Text Summarization**Dragomir Radev, Timothy Allison, Sasha Blair-Goldensohn, et al.*2004 · 400 citations*](https://md.research.elamur.ai/free/en/papers/radev-2004-mead-platform-multidocument-multilingual-text/)
41. [**UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems**Lushan Han, Abhay L. Kashyap, Tim Finin, et al.*2013 · 397 citations*](https://md.research.elamur.ai/free/en/papers/han-2013-umbc-ebiquity-core-semantic-textual/)
42. [**Entity Disambiguation for Knowledge Base Population**Mark Dredze, Paul McNamee, Delip Rao, et al.*2010 · 394 citations*](https://md.research.elamur.ai/free/en/papers/dredze-2010-entity-disambiguation-knowledge-base-population/)
43. [**Emerging Cross-lingual Structure in Pretrained Language Models**Alexis Conneau, Shijie Wu, Haoran Li, et al.*2020 · 378 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2020-emerging-cross-lingual-structure-pretrained/)
44. [**Crowdsourcing Translation: Professional Quality from Non-Professionals**Omar F. Zaidan, Chris Callison-Burch*2011 · 374 citations*](https://md.research.elamur.ai/free/en/papers/zaidan-2011-crowdsourcing-translation-professional-quality-non/)
45. [**cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models**Chris Dyer, Adam Lopez, Juri Ganitkevitch, et al.*2010 · 372 citations*](https://md.research.elamur.ai/free/en/papers/dyer-2010-cdec-decoder-alignment-learning-framework/)
46. [**Improving Lexical Embeddings with Semantic Knowledge**Mo Yu, Mark Dredze*2014 · 371 citations*](https://md.research.elamur.ai/free/en/papers/yu-2014-improving-lexical-embeddings-semantic-knowledge/)
47. [**Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology**Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach, et al.*2019 · 371 citations*](https://md.research.elamur.ai/free/en/papers/zmigrod-2019-counterfactual-data-augmentation-mitigating-gender/)
48. [**The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English**Francisco Guzmán, Peng-Jen Chen, Myle Ott, et al.*2019 · 361 citations*](https://md.research.elamur.ai/free/en/papers/guzman-2019-flores-evaluation-datasets-low-resource/)
49. [**Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference**Benjamin Warner, Antoine Chaffin, Benjamin Clavié, et al.*2024 · 355 citations*](https://md.research.elamur.ai/free/en/papers/warner-2024-smarter-better-faster-longer-modern/)
50. [**Learning Non-Isomorphic Tree Mappings for Machine Translation**Jason Eisner*2003 · 353 citations*](https://md.research.elamur.ai/free/en/papers/eisner-2003-learning-non-isomorphic-tree-mappings/)
51. [**Using “Annotator Rationales” to Improve Machine Learning for Text Categorization**Omar Zaidan, Jason Eisner, Christine Piatko*2007 · 352 citations*](https://md.research.elamur.ai/free/en/papers/zaidan-2007-annotator-rationales-improve-machine-learning/)
52. [**Visual Storytelling**Ting-Hao Kenneth Huang, Francis Ferraro, Nasrin Mostafazadeh, et al.*2016 · 344 citations*](https://md.research.elamur.ai/free/en/papers/huang-2016-visual-storytelling/)
53. [**Problems With Evaluation of Word Embeddings Using Word Similarity Tasks**Manaal Faruqui, Yulia Tsvetkov, Pushpendre Rastogi, et al.*2016 · 332 citations*](https://md.research.elamur.ai/free/en/papers/faruqui-2016-problems-evaluation-word-embeddings-word/)
54. [**ParaCrawl: Web-Scale Acquisition of Parallel Corpora**Marta Bañón, Pinzhen Chen, Barry Haddow, et al.*2020 · 308 citations*](https://md.research.elamur.ai/free/en/papers/banon-2020-paracrawl-web-scale-acquisition-parallel/)
55. [**Extracting Parallel Sentences from Comparable Corpora using Document Level Alignment**Jason R. Smith, Chris Quirk, Kristina Toutanova*2010 · 302 citations*](https://md.research.elamur.ai/free/en/papers/smith-2010-extracting-parallel-sentences-comparable-corpora/)
56. [**Ethical Research Protocols for Social Media Health Research**Adrian Benton, Glen Coppersmith, Mark Dredze*2017 · 292 citations*](https://md.research.elamur.ai/free/en/papers/benton-2017-ethical-research-protocols-social-media/)
57. [**The Arabic Online Commentary Dataset: an Annotated Dataset of Informal Arabic with High Dialectal Content**Omar F. Zaidan, Chris Callison-Burch*2011 · 287 citations*](https://md.research.elamur.ai/free/en/papers/zaidan-2011-arabic-online-commentary-dataset-annotated/)
58. [**Machine Translation of Arabic Dialects**Rabih Zbib, Erika Malchiodi, Jacob Devlin, et al.*2012 · 285 citations*](https://md.research.elamur.ai/free/en/papers/zbib-2012-machine-translation-arabic-dialects/)
59. [**Multi-Sentence Argument Linking**Seth Ebner, Patrick Xia, Ryan Culkin, et al.*2020 · 281 citations*](https://md.research.elamur.ai/free/en/papers/ebner-2020-multi-sentence-argument-linking/)
60. [**Cross-lingual Dependency Parsing Based on Distributed Representations**Jiang Guo, Wanxiang Che, David Yarowsky, et al.*2015 · 269 citations*](https://md.research.elamur.ai/free/en/papers/guo-2015-cross-lingual-dependency-parsing-based/)
61. [**Open Domain Targeted Sentiment**Margaret Mitchell, Jacqui Aguilar, Theresa Wilson, et al.*2013 · 268 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2013-open-domain-targeted-sentiment/)
62. [**Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing**Brian Thompson, Matt Post*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/thompson-2020-automatic-machine-translation-evaluation-many/)
63. [**Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq**Changhan Wang, Yun Tang, Xutai Ma, et al.*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-fairseq-s2t-fast-speech-text/)
64. [**How Can We Accelerate Progress Towards Human-like Linguistic Generalization?**Tal Linzen*2020 · 257 citations*](https://md.research.elamur.ai/free/en/papers/linzen-2020-how-can-we-accelerate-progress/)
65. [**Contrastive Decoding: Open-ended Text Generation as Optimization**Xiang Lisa Li, Ari Holtzman, Daniel Fried, et al.*2022 · 246 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-contrastive-decoding-open-ended-text/)
66. [**Humanity's Last Exam**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 235 citations*](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/)
67. [**CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs**Ahmed El-Kishky, Vishrav Chaudhary, Francisco Guzmán, et al.*2020 · 228 citations*](https://md.research.elamur.ai/free/en/papers/kishky-2020-ccaligned-massive-collection-cross-lingual/)
68. [**Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning**Beliz Gunel, Jingfei Du, Alexis Conneau, et al.*2020 · 216 citations*](https://md.research.elamur.ai/free/en/papers/gunel-2020-supervised-contrastive-learning-pre-trained/)
69. [**Syntactic Data Augmentation Increases Robustness to Inference Heuristics**Junghyun Min, R. Thomas McCoy, Dipanjan Das, et al.*2020 · 215 citations*](https://md.research.elamur.ai/free/en/papers/min-2020-syntactic-data-augmentation-increases-robustness/)
70. [**Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases**Yuval Marton, Chris Callison-Burch, Philip Resnik*2009 · 213 citations*](https://md.research.elamur.ai/free/en/papers/marton-2009-improved-statistical-machine-translation-monolingually/)
71. [**MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets**Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, et al.*2021 · 212 citations*](https://md.research.elamur.ai/free/en/papers/pramanick-2021-momenta-multimodal-framework-detecting-harmful/)
72. [**Universal Decompositional Semantics on Universal Dependencies**Aaron Steven White, Drew Reisinger, Keisuke Sakaguchi, et al.*2016 · 208 citations*](https://md.research.elamur.ai/free/en/papers/white-2016-universal-decompositional-semantics-universal-dependencies/)
73. [**Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation**Haoran Xu, Amr Sharaf, Yunmo Chen, et al.*2024 · 207 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-contrastive-preference-optimization-pushing-boundaries/)
74. [**BART: A Modular Toolkit for Coreference Resolution**Yannick Versley, Simone Paolo Ponzetto, Massimo Poesio, et al.*2008 · 197 citations*](https://md.research.elamur.ai/free/en/papers/versley-2008-bart-modular-toolkit-coreference-resolution/)
75. [**Ordinal Common-sense Inference**Sheng Zhang, Rachel Rudinger, Kevin Duh, et al.*2017 · 192 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2017-ordinal-common-sense-inference/)
76. [**Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation**Adam Poliak, Aparajita Haldar, Rachel Rudinger, et al.*2018 · 190 citations*](https://md.research.elamur.ai/free/en/papers/poliak-2018-collecting-diverse-natural-language-inference/)
77. [**On the Impact of Various Types of Noise on Neural Machine Translation**Huda Khayrallah, Philipp Koehn*2018 · 188 citations*](https://md.research.elamur.ai/free/en/papers/khayrallah-2018-impact-various-types-noise-neural/)
78. [**Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering**Philipp Koehn, Huda Khayrallah, Kenneth Heafield, et al.*2018 · 181 citations*](https://md.research.elamur.ai/free/en/papers/koehn-2018-findings-wmt-2018-shared-task/)
79. [**Findings of the IWSLT 2022 Evaluation Campaign**Antonios Anastasopoulos, Loïc Barrault, Luisa Bentivogli, et al.*2022 · 177 citations*](https://md.research.elamur.ai/free/en/papers/anastasopoulos-2022-findings-iwslt-2022-evaluation-campaign/)
80. [**Findings of the WMT24 General Machine Translation Shared Task: The LLM Era Is Here but MT Is Not Solved Yet**Tom Kocmi, Eleftherios Avramidis, Rachel Bawden, et al.*2024 · 177 citations*](https://md.research.elamur.ai/free/en/papers/kocmi-2024-findings-wmt24-general-machine-translation/)
81. [**Generating Sequences by Learning to Self-Correct**Sean Welleck, Ximing Lu, Peter West, et al.*2022 · 170 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2022-generating-sequences-learning-self-correct/)
82. [**Social Bias in Elicited Natural Language Inferences**Rachel Rudinger, Chandler May, Benjamin Van Durme*2017 · 168 citations*](https://md.research.elamur.ai/free/en/papers/rudinger-2017-social-bias-elicited-natural-language/)
83. [**Parsing Arabic Dialects**David Chiang, Mona Diab, Nizar Habash, et al.*2006 · 166 citations*](https://md.research.elamur.ai/free/en/papers/chiang-2006-parsing-arabic-dialects/)
84. [**Dirt Cheap Web-Scale Parallel Text from the Common Crawl**Jason R. Smith, Herve Saint-Amand, Magdalena Plamada, et al.*2013 · 165 citations*](https://md.research.elamur.ai/free/en/papers/smith-2013-dirt-cheap-web-scale-parallel/)
85. [**Joshua: An Open Source Toolkit for Parsing-Based Machine Translation**Zhifei Li, Chris Callison-Burch, Chris Dyer, et al.*2009 · 163 citations*](https://md.research.elamur.ai/free/en/papers/li-2009-joshua-open-source-toolkit-parsing/)
86. [**Probing What Different NLP Tasks Teach Machines about Function Word Comprehension**Najoung Kim, Roma Patel, Adam Poliak, et al.*2019 · 161 citations*](https://md.research.elamur.ai/free/en/papers/kim-2019-probing-what-different-nlp-tasks/)
87. [**Can GPT-3 Perform Statutory Reasoning?**Andrew Blair-Stanek, Nils Holzenberger, Benjamin Van Durme*2023 · 153 citations*](https://md.research.elamur.ai/free/en/papers/blair-stanek-2023-gpt-3-perform-statutory-reasoning/)
88. [**Unsupervised Quality Estimation for Neural Machine Translation**Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, et al.*2020 · 153 citations*](https://md.research.elamur.ai/free/en/papers/fomicheva-2020-unsupervised-quality-estimation-neural-machine/)
89. [**ChatGPT MT: Competitive for High- (but Not Low-) Resource Languages**Nathaniel Robinson, Perez Ogayo, David R. Mortensen, et al.*2023 · 151 citations*](https://md.research.elamur.ai/free/en/papers/robinson-2023-chatgpt-mt-competitive-high-but/)
90. [**Statistical Power and Translationese in Machine Translation Evaluation**Yvette Graham, Barry Haddow, Philipp Koehn*2020 · 150 citations*](https://md.research.elamur.ai/free/en/papers/graham-2020-statistical-power-translationese-machine-translation/)
91. [**ProsocialDialog: A Prosocial Backbone for Conversational Agents**Hyunwoo Kim, Youngjae Yu, Liwei Jiang, et al.*2022 · 149 citations*](https://md.research.elamur.ai/free/en/papers/kim-2022-prosocialdialog-prosocial-backbone-conversational-agents/)
92. [**SIMULEVAL: An Evaluation Toolkit for Simultaneous Translation**Xutai Ma, Mohammad Javad Dousti, Changhan Wang, et al.*2020 · 144 citations*](https://md.research.elamur.ai/free/en/papers/ma-2020-simuleval-evaluation-toolkit-simultaneous-translation/)
93. [**Vecalign: Improved Sentence Alignment in Linear Time and Space**Brian Thompson, Philipp Koehn*2019 · 144 citations*](https://md.research.elamur.ai/free/en/papers/thompson-2019-vecalign-improved-sentence-alignment-linear/)
94. [**A Comparison of the Events and Relations Across ACE, ERE, TAC-KBP, and FrameNet Annotation Standards**Jacqueline Aguilar, Charley Beller, Paul McNamee, et al.*2014 · 136 citations*](https://md.research.elamur.ai/free/en/papers/aguilar-2014-comparison-events-relations-across-ace/)
95. [**Agent Laboratory: Using LLM Agents as Research Assistants**Samuel Schmidgall, Yusheng Su, Ze Wang, et al.*2025 · 136 citations*](https://md.research.elamur.ai/free/en/papers/schmidgall-2025-agent-laboratory-using-llm/)
96. [**TICO-19: the Translation Initiative for COvid-19**Antonios Anastasopoulos, Alessandro Cattelan, Zi-Yi Dou, et al.*2020 · 133 citations*](https://md.research.elamur.ai/free/en/papers/anastasopoulos-2020-tico-19-translation-initiative-covid/)
97. [**To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning**Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/sprague-2024-cot-cot-chain-thought-helps/)
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