Multilingual Bias Detection and Mitigation for Indian Languages
Ankita Maity Anubhav Sharma Rudra Dhar Tushar Abhishek Manish Gupta Vasudeva Varma
Lack of diverse perspectives causes neutrality bias in Wikipedia content
leading to millions of worldwide readers getting exposed by potentially
inaccurate information. Hence, neutrality bias detection and mitigation is a
critical problem. Although previous studies have proposed effective solutions
for English, no work exists for Indian languages. First, we contribute two
large datasets, mWikiBias and mWNC, covering 8 languages, for the bias
detection and mitigation tasks respectively. Next, we investigate the
effectiveness of popular multilingual Transformer-based models for the two
tasks by modeling detection as a binary classification problem and mitigation
as a style transfer problem. We make the code and data publicly available.