Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Nikita Nangia"'
Autor:
Nikita Nangia, Aymen Almuhaidb, Rajesh N. Keswani, Saihej P. Basra, Abdul A. Aadam, Mary Kwasny, Jasmine Sinha, Srinadh Komanduri
Publikováno v:
Gastro Hep Advances, Vol 3, Iss 1, Pp 128-130 (2024)
Externí odkaz:
https://doaj.org/article/fa7304a14785492387e9661469e3584f
Autor:
Alane Suhr, Clara Vania, Nikita Nangia, Maarten Sap, Mark Yatskar, Samuel R. Bowman, Yoav Artzi
Publikováno v:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::902b87dda1960d315da12d2d1561444f
https://doi.org/10.18653/v1/2021.emnlp-tutorials.1
https://doi.org/10.18653/v1/2021.emnlp-tutorials.1
Publikováno v:
ACL/IJCNLP (1)
Crowdsourcing is widely used to create data for common natural language understanding tasks. Despite the importance of these datasets for measuring and refining model understanding of language, there has been little focus on the crowdsourcing methods
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::254b66cdfa6efafcdc8d62aa514839b8
Autor:
Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, Samuel Bowman
To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average length of about 5,000 tokens, much longer than typical current models can p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::deec7575f0315fb6b9b5b877d5291edc
Autor:
Alicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alexia Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, Samuel R. Bowman
Many crowdsourced NLP datasets contain systematic gaps and biases that are identified only after data collection is complete. Identifying these issues from early data samples during crowdsourcing should make mitigation more efficient, especially when
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::686a1977165b9c1f5d821cf9b89793c2
Publikováno v:
EMNLP (1)
Pretrained language models, especially masked language models (MLMs) have seen success across many NLP tasks. However, there is ample evidence that they use the cultural biases that are undoubtedly present in the corpora they are trained on, implicit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c10abb25c014d1c58dc77367e8505c45
Autor:
Samuel R. Bowman, Nikita Nangia
Publikováno v:
ACL (1)
The GLUE benchmark (Wang et al., 2019b) is a suite of language understanding tasks which has seen dramatic progress in the past year, with average performance moving from 70.0 at launch to 83.9, state of the art at the time of writing (May 24, 2019).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c4e344b81420fe83f5ac1133d99375f
https://doi.org/10.18653/v1/p19-1449
https://doi.org/10.18653/v1/p19-1449
Autor:
Samuel R. Bowman, Nikita Nangia
Publikováno v:
NAACL-HLT (Student Research Workshop)
Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown that, while they perform well on tasks like sentence classification,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23f58c1ea6526f71ada49b67f80d8bed
http://arxiv.org/abs/1804.06028
http://arxiv.org/abs/1804.06028
Publikováno v:
NAACL-HLT
This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. In addition to being one of the largest corpora av
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b7bf1b0a4cd5cf48033bb3ab1f2f95c
http://arxiv.org/abs/1704.05426
http://arxiv.org/abs/1704.05426
Publikováno v:
RepEval@EMNLP
This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017). All
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36e62023e560c03bd83f060bdd78ca2a
https://doi.org/10.18653/v1/w17-5301
https://doi.org/10.18653/v1/w17-5301