Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations (2020)

Abstract: Pretrained language models (PLMs) learn stereotypes held by humans and reflected in text from their training corpora, including gender bias. When PLMs are used for downstream tasks such as picking candidates for a job, people’s lives can be negatively affected by these learned stereotypes. Prior work usually identifies a linear gender subspace and removes gender information by eliminating the subspace. Following this line of work, we propose to use DensRay, an analytical method for obtaining interpretable dense subspaces. We show that DensRay performs on-par with prior approaches, but provide arguments that it is more robust and provide indications that it preserves language model performance better. By applying DensRay to attention heads and layers of BERT we show that gender information is spread across all attention heads and most of the layers. Also we show that DensRay can obtain gender bias scores on both token and sentence levels. Finally, we demonstrate that we can remove bias multilingually, e.g., from Chinese, using only English training data.

## conclusion we introduced densray debiasing on bert. our experiments showed that this method can effectively mitigate gender bias on occtmp and the association tests, while maintaining the performance of bert on language modeling and glue tasks. we applied densray to bert attention heads and showed that gender information is processed in all attention heads: there is no single attention head responsible for processing gender information. we also used densray to obtain interpretable gender bias scores, to quantify bias on token and sentence levels for all representations. finally, we demonstrated that we can remove bias multilingually: we used only english training data to effectively debias chinese. in future work we plan to investigate multilingual transfer of debiasing techniques more closely. further, debiasing methods should not rely on the binary signals "male"-"female" but rather on multiple sets of attributes. thus we plan to investigate debiasing with non-binary approaches.