A Multilingual Perspective on Probing Gender Bias

TL;DR

This paper explores how gender bias manifests across multiple languages and cultures, using interdisciplinary methods to analyze language and models, highlighting societal impacts and the importance of a multilingual perspective.

Contribution

It extends gender bias research to multilingual contexts, integrating NLP with political science and history for a comprehensive analysis.

Findings

Gender bias exists across languages and cultures.

Multilingual models reveal diverse bias patterns.

Interdisciplinary approach enhances understanding of societal biases.

Abstract

Gender bias represents a form of systematic negative treatment that targets individuals based on their gender. This discrimination can range from subtle sexist remarks and gendered stereotypes to outright hate speech. Prior research has revealed that ignoring online abuse not only affects the individuals targeted but also has broader societal implications. These consequences extend to the discouragement of women's engagement and visibility within public spheres, thereby reinforcing gender inequality. This thesis investigates the nuances of how gender bias is expressed through language and within language technologies. Significantly, this thesis expands research on gender bias to multilingual contexts, emphasising the importance of a multilingual and multicultural perspective in understanding societal biases. In this thesis, I adopt an interdisciplinary approach, bridging natural language processing with other disciplines such as political science and history, to probe gender bias in natural language and language models.