WIKIBIAS: Detecting Multi-Span Subjective Biases in Language
Zhong Yang, Jingfeng Yang, Wei Hong Xu, Diyi Yang · 2021
Biases continue to be prevalent in modern text and media, especially subjective bias -a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth.To tackle the problem of detecting and further mitigating subjective bias, we introduce a manually annotated parallel corpus WIKIBIAS with more than 4,000 sentence pairs from Wikipedia edits.This corpus contains annotations towards both sentencelevel bias types and token-level biased segments.We present systematic analyses of our dataset and results achieved by a set of state-ofthe-art baselines in terms of three tasks: bias classification, tagging biased segments, and neutralizing biased text.We find that current models still struggle with detecting multi-span biases despite their reasonable performances, suggesting that our dataset can serve as a useful research benchmark.We also demonstrate that models trained on our dataset can generalize well to multiple domains such as news and political speeches. 1