Analyzing ethnic or religious bias is important for improving fairness, accountability, and transparency of natural language processing models. However, many techniques rely on human-compiled lists of bias terms, which are expensive to create and are limited in coverage. In this study, we present a fully data-driven pipeline for generating a knowledge graph (KG) of cultural knowledge and stereotypes. Our resulting KG covers 5 religious groups and 5 nationalities and can easily be extended to include more entities. Our human evaluation shows that the majority (59.2%) of non-singleton entries are coherent and complete stereotypes. We further show that performing intermediate masked language model training on the verbalized KG leads to a higher level of cultural awareness in the model and has the potential to increase classification performance on knowledge-crucial samples on a related task, i.e., hate speech detection.

翻译：分析族裔或宗教偏见对于提高自然语言处理模式的公平性、问责制和透明度十分重要,但是,许多技术依赖由人组成的偏见术语清单,这些术语对于创造成本昂贵,而且覆盖面有限。在本研究报告中,我们为制作文化知识和定型观念的知识图表提供了完全由数据驱动的管道,由此产生的知识图表覆盖了5个宗教团体和5个民族,可以很容易地扩大到包括更多的实体。我们的人类评价表明,大多数非单词条目(59.2%)是连贯和完整的定型观念。我们进一步表明,在口头翻译的KG上进行中间蒙面语言模式培训,可以提高模型的文化意识水平,并有可能提高相关任务(即仇恨言论检测)知识标记样本的分类性能。