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Improving BERT-based Query-by-Document Retrieval with Multi-task Optimization Read chapter Read first chapter

2022 | OriginalPaper | Chapter

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

Authors : Amin Bigdeli, Negar Arabzadeh, Shirin Seyedsalehi, Morteza Zihayat, Ebrahim Bagheri

Published in: Advances in Information Retrieval

Publisher: Springer International Publishing

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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.

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previous chapter Evaluating the Use of Synthetic Queries for Pre-training a Semantic Query Tagger
next chapter Recommender Systems: When Memory Matters
Footnotes
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Metadata
Title
A Light-Weight Strategy for Restraining Gender Biases in Neural Rankers
Authors
Amin Bigdeli
Negar Arabzadeh
Shirin Seyedsalehi
Morteza Zihayat
Ebrahim Bagheri
Copyright Year
2022
Book
Advances in Information Retrieval

Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-99739-7_6