STATE ToxiCN：面向中文仇恨言论检测的跨度级目标感知毒性提取基准
STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detection
AI总结：
针对中文仇恨言论检测研究滞后、缺乏跨度级细粒度标注及仇恨俚语研究的问题，本文构建了首个跨度级中文仇恨言论数据集STATE ToxiCN，评估了现有模型检测性能并首次研究中文仇恨俚语的LLM检测能力，为相关研究提供资源与参考。
摘要：
仇恨言论的泛滥已对社会造成严重危害。仇恨的强度与指向性同其关联的目标和论据密切相关。然而，中文仇恨言论检测研究相对滞后，现有数据集缺乏跨度级的细粒度标注。此外，中文仇恨俚语相关研究的缺失也带来了重大挑战。本文提出了一种中文仇恨言论细粒度检测解决方案。首先，我们构建了包含目标-论据-仇恨群体四元组的数据集STATE ToxiCN，这是首个跨度级中文仇恨言论数据集。其次，我们利用STATE ToxiCN评估了现有模型的跨度级仇恨言论检测性能。最后，我们首次开展了中文仇恨俚语研究，并评估了大语言模型（LLMs）检测此类表达的能力。本研究为推动中文跨度级仇恨言论检测提供了宝贵资源与洞见。
英文摘要：
The proliferation of hate speech has caused significant harm to society. The intensity and directionality of hate are closely tied to the target and argument it is associated with. However, research on hate speech detection in Chinese has lagged behind, and existing datasets lack span-level fine-grained annotations. Furthermore, the lack of research on Chinese hateful slang poses a significant challenge. In this paper, we provide a solution for fine-grained detection of Chinese hate speech. First, we construct a dataset containing Target-Argument-Hateful-Group quadruples (STATE ToxiCN), which is the first span-level Chinese hate speech dataset. Secondly, we evaluate the span-level hate speech detection performance of existing models using STATE ToxiCN. Finally, we conduct the first study on Chinese hateful slang and evaluate the ability of LLMs to detect such expressions. Our work contributes valuable resources and insights to advance span-level hate speech detection in Chinese.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
