论文标题
在隐性仇恨言论检测中利用世界知识
Leveraging World Knowledge in Implicit Hate Speech Detection
论文作者
论文摘要
尽管已经非常关注明确的仇恨言论，但用编码或间接语言掩饰的隐性仇恨表达无处不在，并且仍然是现有仇恨言语检测系统的主要挑战。本文提出了第一次尝试将链接（EL）技术链接到明确和隐性仇恨言语检测的尝试，我们表明，关于实体在文本中提到的有关实体的知识确实有助于模型更好地检测仇恨言论，并且将其添加到模型中的好处在显式实体触发时更为明显（例如，Rally，rally，Kkk）。我们还讨论了现实世界知识不会为仇恨言论检测增加价值的案例，这为理解和建模仇恨言论的微妙之处提供了更多的见解。
While much attention has been paid to identifying explicit hate speech, implicit hateful expressions that are disguised in coded or indirect language are pervasive and remain a major challenge for existing hate speech detection systems. This paper presents the first attempt to apply Entity Linking (EL) techniques to both explicit and implicit hate speech detection, where we show that such real world knowledge about entity mentions in a text does help models better detect hate speech, and the benefit of adding it into the model is more pronounced when explicit entity triggers (e.g., rally, KKK) are present. We also discuss cases where real world knowledge does not add value to hate speech detection, which provides more insights into understanding and modeling the subtleties of hate speech.
