Resolving Gendered Ambiguous Pronouns with BERT

Ionita, Matei; Kashnitsky, Yury; Krige, Ken; Larin, Volodymyr; Logvinenko, Dennis; Atanasov, Atanas

doi:10.18653/v1/w19-3817

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…As a result, we excluded them from our counts for techniques as well. We cite the papers here; most propose techniques we would have categorized as "Questionable correlations," with a few as "Other representational harms" (Abzaliev, 2019; Attree, 2019; Bao and Qiao, 2019;Chada, 2019;Ionita et al, 2019;Lois et al, 2019;Wang, 2019;Xu and Yang, 2019;Yang et al, 2019).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Language (Technology) is Power: A Critical Survey of “Bias” in NLP

Blodgett¹,

Barocas²,

Daumé³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

449

223

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We survey 146 papers analyzing "bias" in NLP systems, fnding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further fnd that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these fndings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"-i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements-and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.

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Section: Acknowledgmentsmentioning

confidence: 99%

Language (Technology) is Power: A Critical Survey of “Bias” in NLP

Blodgett¹,

Barocas²,

Daumé³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

449

223

View full text Add to dashboard Cite

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“…Instead, Ionita et al (2019) extracted BERT embeddings from specific layers of BERT and used an array of coreference predictions and hand crafted features on their implementation. Liu (2019) introduced a data augmentation technique in which he replaced all names in the dataset to inject anonimity and make the model less biased towards the names themselves.…”

Section: Pronoun Resolutionmentioning

confidence: 99%

A Neural Entity Coreference Resolution Review

Stylianou,

Vlahavas

2019

Preprint

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Entity Coreference Resolution is the task of resolving all the mentions in a document that refer to the same real world entity and is considered as one of the most difficult tasks in natural language understanding. While in it is not an end task, it has been proved to improve downstream natural language processing tasks such as entity linking, machine translation, summarization and chatbots. We conducted a systematic a review of neural-based approached and provide a detailed appraisal of the datasets and evaluation metrics in the field. Emphasis is given on Pronoun Resolution, a subtask of Coreference Resolution, which has seen various improvements in the recent years. We conclude the study by highlight the lack of agreed upon standards and propose a way to expand the task even further.

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