@inproceedings{zhong-etal-2021-wikibias-detecting,
    title = "{WIKIBIAS}: Detecting Multi-Span Subjective Biases in Language",
    author = "Zhong, Yang  and
      Yang, Jingfeng  and
      Xu, Wei  and
      Yang, Diyi",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.findings-emnlp.155/",
    doi = "10.18653/v1/2021.findings-emnlp.155",
    pages = "1799--1814",
    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."
}