Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification
Abstract
Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined. In this work, we treat the gender as domains (e.g., male vs. female) and present a standard domain adaptation model to reduce the gender bias and improve performance of text classifiers under multilingual settings. We evaluate our approach on two text classification tasks, hate speech detection and rating prediction, and demonstrate the effectiveness of our approach with three fair-aware baselines.
Publication Title
NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
Recommended Citation
Huang, X. (2022). Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification. NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 717-723. Retrieved from https://digitalcommons.memphis.edu/facpubs/17953
