Extensive study on the underlying gender bias in contextualized word embeddings

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

Abstract: Gender bias is affecting many natural language processing applications. While we are still far from proposing debiasing methods that will solve the problem, we are making progress analyzing the impact of this bias in current algorithms. This paper provides an extensive study of the underlying gender bias in popular contextualized word embeddings. Our study provides an insightful analysis of evaluation measures applied to several English data domains and the layers of the contextualized word embeddings. It is also adapted and extended to the Spanish language. Our study points out the advantages and limitations of the various evaluation measures that we are using and aims to standardize the evaluation of gender bias in contextualized word embeddings.