Related papers: Gender Bias Mitigation for Bangla Classification Tasks

Gender Bias Mitigation for Bangla Classification Tasks

URL: http://arxiv.org/abs/2411.10636v1
Date: Sat, 16 Nov 2024 00:04:45 GMT
Title: Gender Bias Mitigation for Bangla Classification Tasks
Authors: Sajib Kumar Saha Joy, Arman Hassan Mahy, Meherin Sultana, Azizah Mamun Abha, MD Piyal Ahmmed, Yue Dong, G M Shahariar,
Abstract summary: We investigate gender bias in Bangla pretrained language models. By altering names and gender-specific terms, we ensured these datasets were suitable for detecting and mitigating gender bias.
Score: 2.6285986998314783
License:
Abstract: In this study, we investigate gender bias in Bangla pretrained language models, a largely under explored area in low-resource languages. To assess this bias, we applied gender-name swapping techniques to existing datasets, creating four manually annotated, task-specific datasets for sentiment analysis, toxicity detection, hate speech detection, and sarcasm detection. By altering names and gender-specific terms, we ensured these datasets were suitable for detecting and mitigating gender bias. We then proposed a joint loss optimization technique to mitigate gender bias across task-specific pretrained models. Our approach was evaluated against existing bias mitigation methods, with results showing that our technique not only effectively reduces bias but also maintains competitive accuracy compared to other baseline approaches. To promote further research, we have made both our implementation and datasets publicly available https://github.com/sajib-kumar/Gender-Bias-Mitigation-From-Bangla-PLM

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