Gabriel Stanovsky

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Ido DaganProfessor, Computer Science Department, Bar-Ilan UniversityVerified email at cs.biu.ac.il
Luke ZettlemoyerUniversity of Washington; MetaVerified email at cs.washington.edu
Roy SchwartzAssociate Professor, the School of Computer Science, the Hebrew University of JerusalemVerified email at mail.huji.ac.il
Noah A. SmithUniversity of Washington; Allen Institute for Artificial IntelligenceVerified email at cs.washington.edu
Matt GardnerScaled CognitionVerified email at scaledcognition.com
Sameer SinghProfessor, UC Irvine | CTO, Spiffy AIVerified email at uci.edu
Julian MichaelNew York UniversityVerified email at nyu.edu
Yonatan BittonResearch Scientist, GoogleVerified email at google.com
Iryna GurevychFull Professor, TU Darmstadt; Adjunct Professor, MBZUAI, UAE; Affiliated Professor, INSAIT, BulgariaVerified email at cs.tu-darmstadt.de
Dheeru DuaUniversity of California, IrvineVerified email at uci.edu
Pradeep DasigiAllen Institute for AI (Ai2)Verified email at allenai.org
Yizhong WangUniversity of WashingtonVerified email at cs.washington.edu
Arie CattanBar-Ilan UniversityVerified email at biu.ac.il
Dafna ShahafAssociate Professor of Computer Science, The Hebrew University of JerusalemVerified email at cs.huji.ac.il
Tomasz LimisiewiczPh.D. Candidate, Institute of Formal and Applied Linguistics, Charles University, Prague, CzechVerified email at ufal.mff.cuni.cz
Dan MalkinGoogle ResearchVerified email at mail.huji.ac.il
Mandar JoshiGoogle AIVerified email at google.com
Alon EirewBar Ilan UniversityVerified email at live.biu.ac.il
Jonathan BraggAllen Institute for AI (AI2)Verified email at allenai.org
Daniel KhashabiJohns Hopkins UniversityVerified email at jhu.edu

Gabriel Stanovsky

The Hebrew University of Jerusalem

Verified email at mail.huji.ac.il - Homepage

Computational Linguistics


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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs D Dua, Y Wang, P Dasigi, G Stanovsky, S Singh, M Gardner arXiv preprint arXiv:1903.00161, 2019	923	2019
Evaluating gender bias in machine translation G Stanovsky, NA Smith, L Zettlemoyer arXiv preprint arXiv:1906.00591, 2019	498	2019
Supervised open information extraction G Stanovsky, J Michael, L Zettlemoyer, I Dagan Proceedings of the 2018 Conference of the North American Chapter of the …, 2018	374	2018
Creating a large benchmark for open information extraction G Stanovsky, I Dagan Proceedings of the 2016 Conference on Empirical Methods in Natural Language …, 2016	177	2016
The right tool for the job: Matching model and instance complexities R Schwartz, G Stanovsky, S Swayamdipta, J Dodge, NA Smith arXiv preprint arXiv:2004.07453, 2020	175	2020
Evaluating question answering evaluation A Chen, G Stanovsky, S Singh, M Gardner Proceedings of the 2nd workshop on machine reading for question answering …, 2019	106	2019
Crowdsourcing question-answer meaning representations J Michael, G Stanovsky, L He, I Dagan, L Zettlemoyer arXiv preprint arXiv:1711.05885, 2017	90	2017
Open IE as an Intermediate Structure for Semantic Tasks G Stanovsky, I Dagan, Mausam Association for Computational Linguistics, 303-308, 2015	89	2015
State of what art? a call for multi-prompt llm evaluation M Mizrahi, G Kaplan, D Malkin, R Dror, D Shahaf, G Stanovsky Transactions of the Association for Computational Linguistics 12, 933-949, 2024	78	2024
Getting more out of syntax with props G Stanovsky, J Ficler, I Dagan, Y Goldberg arXiv preprint arXiv:1603.01648, 2016	76	2016
Recognizing mentions of adverse drug reaction in social media using knowledge-infused recurrent models G Stanovsky, D Gruhl, P Mendes Proceedings of the 15th Conference of the European Chapter of the …, 2017	73	2017
Genie: A leaderboard for human-in-the-loop evaluation of text generation D Khashabi, G Stanovsky, J Bragg, N Lourie, J Kasai, Y Choi, NA Smith, ... arXiv preprint arXiv:2101.06561, 2021	69	2021
Collecting a large-scale gender bias dataset for coreference resolution and machine translation S Levy, K Lazar, G Stanovsky arXiv preprint arXiv:2109.03858, 2021	68	2021
Gender trends in computer science authorship LL Wang, G Stanovsky, L Weihs, O Etzioni Communications of the ACM 64 (3), 78-84, 2021	66	2021
Breaking common sense: Whoops! a vision-and-language benchmark of synthetic and compositional images N Bitton-Guetta, Y Bitton, J Hessel, L Schmidt, Y Elovici, G Stanovsky, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023	61	2023
Controlled crowdsourcing for high-quality QA-SRL annotation P Roit, A Klein, D Stepanov, J Mamou, J Michael, G Stanovsky, ... arXiv preprint arXiv:1911.03243, 2019	55	2019
Integrating deep linguistic features in factuality prediction over unified datasets G Stanovsky, J Eckle-Kohler, Y Puzikov, I Dagan, I Gurevych Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017	54	2017
MOCHA: A dataset for training and evaluating generative reading comprehension metrics A Chen, G Stanovsky, S Singh, M Gardner arXiv preprint arXiv:2010.03636, 2020	50	2020
Gender coreference and bias evaluation at wmt 2020 T Kocmi, T Limisiewicz, G Stanovsky Proceedings of the Fifth Conference on Machine Translation, 357-364, 2020	43	2020
Cross-document coreference resolution over predicted mentions A Cattan, A Eirew, G Stanovsky, M Joshi, I Dagan arXiv preprint arXiv:2106.01210, 2021	39	2021

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