Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification

• URL: http://arxiv.org/abs/2204.05459v1
• Date: Tue, 12 Apr 2022 01:15:36 GMT
• Title: Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification
• Authors: Xiaolei Huang
• Abstract summary: We treat the gender as domains and present a standard domain adaptation model to reduce the gender bias. We evaluate our approach on two text classification tasks, hate speech detection and rating prediction, and demonstrate the effectiveness of our approach.
• Score: 8.137681060429527
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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