Hate speech is a challenging issue plaguing the online social media. While better models for hate speech detection are continuously being developed, there is little research on the bias and interpretability aspects of hate speech. In this paper, we introduce HateXplain, the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in our dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling decision (as hate, offensive or normal) is based. We utilize existing state-of-the-art models and observe that even models that perform very well in classification do not score high on explainability metrics like model plausibility and faithfulness. We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities. We have made our code and dataset public at https://github.com/punyajoy/HateXplain

本文介绍了 HateXplain，这是一项针对多个问题方面的第一个基准仇恨言论数据集，使用基本、常用的 3 级分类、目标社区和理由对数据集中的每个帖子进行了注释。我们利用现有的最先进模型观察到，即使在分类方面表现非常好的模型在模型合理性和忠实度等可解释度指标上也得分不高。此外，我们还观察到，利用人类理由进行训练的模型在减少对目标社区的非预期偏见方面表现更好。

HateXplain: 可解释仇恨言论检测的基准数据集