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Extensive study on the underlying gender bias in contextualized word embeddings · Study

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ArticleExtensive study on the underlying gender bias in contextualized word embeddings

Christine Basta2020

Article2020

Extensive study on the underlying gender bias in contextualized word embeddings

Christine Basta · Marta R. Costa‐jussà · Noé Casas

25 citations·0/0 files stored·Neural Computing and Applications

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Ethics and Social Impacts of AI Topic Modeling Gender Studies in Language

Abstract

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Citations in this library

Stored only when both papers are in the corpus — 25 citations exist upstream.

References

2018Mitigating Unwanted Biases with Adversarial Learning 2020Language (Technology) is Power: A Critical Survey of “Bias” in NLP 2019On Measuring Social Biases in Sentence Encoders 2019Measuring Bias in Contextualized Word Representations 2019Gender Bias in Contextualized Word Embeddings 2020Gender Bias in Neural Natural Language Processing 2019Evaluating the Underlying Gender Bias in Contextualized Word Embeddings 2021Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases

Details

Year2020

Typearticle

Published2020-07-24

VenueNeural Computing and Applications

ISSN-L0941-0643

Languageen

Citations25

Files0 public / 0

ShelfEthics and Social Impacts of AI

Identifiers

doi10.1007/s00521-020-05211-z

openalex_workW3044663232

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