A Light-Weight Strategy for Restraining Gender Biases in Neural Rankers

Abstract

In light of recent studies that show neural retrieval methods may intensify gender biases during retrieval, the objective of this paper is to propose a simple yet effective sampling strategy for training neural rankers that would allow the rankers to maintain their retrieval effectiveness while reducing gender biases. Our work proposes to consider the degrees of gender bias when sampling documents to be used for training neural rankers. We report our findings on the MS MARCO collection and based on different query datasets released for this purpose in the literature. Our results show that the proposed light-weight strategy can show competitive (or even better) performance compared to the state-of-the-art neural architectures specifically designed to reduce gender biases.