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Nikita Nangia
Nikita Nangia
Verifisert e-postadresse på nyu.edu
Tittel
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Sitert av
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A broad-coverage challenge corpus for sentence understanding through inference
A Williams, N Nangia, SR Bowman
arXiv preprint arXiv:1704.05426, 2017
43452017
Superglue: A stickier benchmark for general-purpose language understanding systems
A Wang, Y Pruksachatkun, N Nangia, A Singh, J Michael, F Hill, O Levy, ...
Advances in neural information processing systems 32, 2019
20782019
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
8752022
CrowS-pairs: A challenge dataset for measuring social biases in masked language models
N Nangia, C Vania, R Bhalerao, SR Bowman
arXiv preprint arXiv:2010.00133, 2020
4902020
BBQ: A hand-built bias benchmark for question answering
A Parrish, A Chen, N Nangia, V Padmakumar, J Phang, J Thompson, ...
arXiv preprint arXiv:2110.08193, 2021
1912021
Listops: A diagnostic dataset for latent tree learning
N Nangia, SR Bowman
arXiv preprint arXiv:1804.06028, 2018
1292018
The repeval 2017 shared task: Multi-genre natural language inference with sentence representations
N Nangia, A Williams, A Lazaridou, SR Bowman
arXiv preprint arXiv:1707.08172, 2017
1072017
Human vs. muppet: A conservative estimate of human performance on the GLUE benchmark
N Nangia, SR Bowman
arXiv preprint arXiv:1905.10425, 2019
1062019
QuALITY: Question answering with long input texts, yes!
RY Pang, A Parrish, N Joshi, N Nangia, J Phang, A Chen, V Padmakumar, ...
arXiv preprint arXiv:2112.08608, 2021
802021
jiant 1.2: A software toolkit for research on general-purpose text understanding models
A Wang, IF Tenney, Y Pruksachatkun, K Yu, J Hula, P Xia, R Pappagari, ...
Note: http://jiant. info/Cited by: footnote 4, 2019
532019
Does putting a linguist in the loop improve NLU data collection?
A Parrish, W Huang, O Agha, SH Lee, N Nangia, A Warstadt, K Aggarwal, ...
arXiv preprint arXiv:2104.07179, 2021
322021
What ingredients make for an effective crowdsourcing protocol for difficult NLU data collection tasks?
N Nangia, S Sugawara, H Trivedi, A Warstadt, C Vania, SR Bowman
arXiv preprint arXiv:2106.00794, 2021
282021
What do nlp researchers believe? results of the nlp community metasurvey
J Michael, A Holtzman, A Parrish, A Mueller, A Wang, A Chen, D Madaan, ...
arXiv preprint arXiv:2208.12852, 2022
242022
The multi-genre nli corpus
A Williams, N Nangia, SR Bowman
172018
A broad-coverage challenge corpus for sentence understanding through inference. arXiv 2017
A Williams, N Nangia, SR Bowman
arXiv preprint arXiv:1704.05426, 0
17
Single-turn debate does not help humans answer hard reading-comprehension questions
A Parrish, H Trivedi, E Perez, A Chen, N Nangia, J Phang, SR Bowman
arXiv preprint arXiv:2204.05212, 2022
122022
What Makes Reading Comprehension Questions Difficult?
S Sugawara, N Nangia, A Warstadt, SR Bowman
arXiv preprint arXiv:2203.06342, 2022
102022
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions
A Parrish, H Trivedi, N Nangia, V Padmakumar, J Phang, AS Saimbhi, ...
arXiv preprint arXiv:2210.10860, 2022
82022
Crowdsourcing beyond annotation: Case studies in benchmark data collection
A Suhr, C Vania, N Nangia, M Sap, M Yatskar, S Bowman, Y Artzi
Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021
82021
Discrete latent structure in neural networks
V Niculae, CF Corro, N Nangia, T Mihaylova, AFT Martins
arXiv preprint arXiv:2301.07473, 2023
62023
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Artikler 1–20