Mitigating Gender Bias Amplification in Distribution by Posterior Regularization

Abstract

Advanced machine learning techniques have boosted the performance of natural language processing. Nevertheless, recent studies, e.g., However, their analysis is conducted only on models' top predictions. In this paper, we investigate the gender bias amplification issue from the distribution perspective and demonstrate that the bias is amplified in the view of predicted probability distribution over labels. We further propose a bias mitigation approach based on posterior regularization. With little performance loss, our method can almost remove the bias amplification in the distribution. Our study sheds the light on understanding the bias amplification.

Topics & Concepts