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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.155</identifier>
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%0 Conference Proceedings
%T WIKIBIAS: Detecting Multi-Span Subjective Biases in Language
%A Zhong, Yang
%A Yang, Jingfeng
%A Xu, Wei
%A Yang, Diyi
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F zhong-etal-2021-wikibias-detecting
%X 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.
%R 10.18653/v1/2021.findings-emnlp.155
%U https://aclanthology.org/2021.findings-emnlp.155
%U https://doi.org/10.18653/v1/2021.findings-emnlp.155
%P 1799-1814

Markdown (Informal)

[WIKIBIAS: Detecting Multi-Span Subjective Biases in Language](https://aclanthology.org/2021.findings-emnlp.155) (Zhong et al., Findings 2021)

• WIKIBIAS: Detecting Multi-Span Subjective Biases in Language (Zhong et al., Findings 2021)

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.