Gabriel Stanovsky
Gabriel Stanovsky
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Cited by
Cited by
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
Evaluating gender bias in machine translation
G Stanovsky, NA Smith, L Zettlemoyer
arXiv preprint arXiv:1906.00591, 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
Creating a large benchmark for open information extraction
G Stanovsky, I Dagan
Proceedings of the 2016 Conference on Empirical Methods in Natural Language …, 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
Crowdsourcing question-answer meaning representations
J Michael, G Stanovsky, L He, I Dagan, L Zettlemoyer
arXiv preprint arXiv:1711.05885, 2017
Open IE as an Intermediate Structure for Semantic Tasks
G Stanovsky, I Dagan, Mausam
Association for Computational Linguistics, 303-308, 2015
Evaluating question answering evaluation
A Chen, G Stanovsky, S Singh, M Gardner
Proceedings of the 2nd workshop on machine reading for question answering …, 2019
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
Getting more out of syntax with props
G Stanovsky, J Ficler, I Dagan, Y Goldberg
arXiv preprint arXiv:1603.01648, 2016
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
Gender trends in computer science authorship
LL Wang, G Stanovsky, L Weihs, O Etzioni
Communications of the ACM 64 (3), 78-84, 2021
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
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
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
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
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
Streamlining cross-document coreference resolution: Evaluation and modeling
A Cattan, A Eirew, G Stanovsky, M Joshi, I Dagan
arXiv preprint arXiv:2009.11032, 2020
SemEval-2019 task 10: math question answering
M Hopkins, R Le Bras, C Petrescu-Prahova, G Stanovsky, H Hajishirzi, ...
Proceedings of the 13th International Workshop on Semantic Evaluation, 893-899, 2019
Automatic generation of contrast sets from scene graphs: Probing the compositional consistency of GQA
Y Bitton, G Stanovsky, R Schwartz, M Elhadad
arXiv preprint arXiv:2103.09591, 2021
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