WIKIBIAS: Detecting Multi-Span Subjective Biases in Language

Yang Zhong, Jingfeng Yang, Wei Xu, Diyi Yang


Abstract
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 sentence-level bias types and token-level biased segments. We present systematic analyses of our dataset and results achieved by a set of state-of-the-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.
Anthology ID:
2021.findings-emnlp.155
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1799–1814
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.155
DOI:
10.18653/v1/2021.findings-emnlp.155
Bibkey:
Cite (ACL):
Yang Zhong, Jingfeng Yang, Wei Xu, and Diyi Yang. 2021. WIKIBIAS: Detecting Multi-Span Subjective Biases in Language. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1799–1814, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
WIKIBIAS: Detecting Multi-Span Subjective Biases in Language (Zhong et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.155.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.155.mp4
Code
 cs329yangzhong/wikibias