Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns

Autor: Jason Baldridge, Kellie Webster, Vera Axelrod, Marta Recasens

Publikováno v: Transactions of the Association for Computational Linguistics. 6:605-617

Coreference resolution is an important task for natural language understanding, and the resolution of ambiguous pronouns a longstanding challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in sufficient volume or diversity to ac

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5dc3c3c773bf0cdea2291e8baa9fae1b
https://doi.org/10.1162/tacl_a_00240

Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias

Autor: Anders Søgaard, Ana Valeria Gonzalez, Kellie Webster, Rasmus Hvingelby, Maria Barrett

Publikováno v: EMNLP (1)
Gonzalez, A V, Barrett, M J, Hvingelby, R, Søgaard, A & Webster, K 2020, Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias . in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, pp. 2637–2648, The 2020 Conference on Empirical Methods in Natural Language Processing, 16/11/2020 . https://doi.org/10.18653/v1/2020.emnlp-main.209

The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and WinoGender highlight model preferences that are "hallucinatory", e.g., disambiguating

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f97c8d7d459da02681103c9014af0d7
https://doi.org/10.18653/v1/2020.emnlp-main.209

Social Biases in NLP Models as Barriers for Persons with Disabilities

Autor: Ben Hutchinson, Yu Zhong, Kellie Webster, Stephen Denuyl, Emily Denton, Vinodkumar Prabhakaran

Publikováno v: ACL

Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models. In particular, representations encoded in models often inadvertently perpetuate undesirable social biases from t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b78fd580006d6cb407603f0bafb224f5
https://doi.org/10.18653/v1/2020.acl-main.487

Unintended machine learning biases as social barriers for persons with disabilitiess

Autor: Vinodkumar Prabhakaran, Stephen Denuyl, Ben Hutchinson, Yu Zhong, Emily Denton, Kellie Webster

Publikováno v: ACM SIGACCESS Accessibility and Computing. :1-1

Persons with disabilities face many barriers to full participation in society, and the rapid advancement of technology has the potential to create ever more. Building equitable and inclusive technologies for people with disabilities demands paying at

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3d5dd8c1b8514861caf44f8d881c7eb5
https://doi.org/10.1145/3386296.3386305

Using mention accessibility to improve coreference resolution

Autor: Kellie Webster, Joel Nothman

Publikováno v: ACL (2)

Modern coreference resolution systems require linguistic and general knowledge typically sourced from costly, manually curated resources. Despite their intuitive appeal, results have been mixed. In this work, we instead implement fine-grained surface

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::27b4833a3113938ddd1dee0d9dcec353
https://doi.org/10.18653/v1/p16-2070