facebook/md_gender_bias

Tasks

• text-classification

Citation text from the card

@inproceedings{dinan-etal-2020-multi,
    title = "Multi-Dimensional Gender Bias Classification",
    author = "Dinan, Emily  and
      Fan, Angela  and
      Wu, Ledell  and
      Weston, Jason  and
      Kiela, Douwe  and
      Williams, Adina",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.23",
    doi = "10.18653/v1/2020.emnlp-main.23",
    pages = "314--331",
    abstract = "Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a novel, general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information. In addition, we collect a new, crowdsourced evaluation benchmark. Distinguishing between gender bias along multiple dimensions enables us to train better and more fine-grained gender bias classifiers. We show our classifiers are valuable for a variety of applications, like controlling for gender bias in g

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