Evaluating the Underlying Gender Bias in Contextualized Word Embeddings

Christine Basta, Marta R. Costa-jussà, Noe Casas


Abstract
Gender bias is highly impacting natural language processing applications. Word embeddings have clearly been proven both to keep and amplify gender biases that are present in current data sources. Recently, contextualized word embeddings have enhanced previous word embedding techniques by computing word vector representations dependent on the sentence they appear in. In this paper, we study the impact of this conceptual change in the word embedding computation in relation with gender bias. Our analysis includes different measures previously applied in the literature to standard word embeddings. Our findings suggest that contextualized word embeddings are less biased than standard ones even when the latter are debiased.
Anthology ID:
W19-3805
Volume:
Proceedings of the First Workshop on Gender Bias in Natural Language Processing
Month:
August
Year:
2019
Address:
Florence, Italy
Venue:
GeBNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
33–39
Language:
URL:
https://aclanthology.org/W19-3805
DOI:
10.18653/v1/W19-3805
Bibkey:
Cite (ACL):
Christine Basta, Marta R. Costa-jussà, and Noe Casas. 2019. Evaluating the Underlying Gender Bias in Contextualized Word Embeddings. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, pages 33–39, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings (Basta et al., GeBNLP 2019)
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PDF:
https://aclanthology.org/W19-3805.pdf