细粒度中文仇恨言论理解：片段级资源、暗语词典与增强检测框架
Fine-Grained Chinese Hate Speech Understanding: Span-Level Resources, Coded Term Lexicon, and Enhanced Detection Frameworks
AI总结：
针对中文仇恨言论检测可解释性研究面临的片段级标注数据稀缺和暗语识别不足两大挑战，本文提出了首个片段级中文仇恨言论数据集STATE ToxiCN，全面研究了LLMs对中文暗语仇恨词汇的语义解释能力，并提出一种整合标注词典的方法显著提升了检测性能。
摘要：
仇恨言论的泛滥造成了严重的社会危害，其强度和方向性与特定目标和论点密切相关。近年来，人们开发了大量基于机器学习的方法来自动检测在线平台上的仇恨评论。然而，中文仇恨言论检测研究相对滞后，可解释性研究面临两大挑战：首先，片段级细粒度标注数据集的稀缺限制了模型对仇恨言论的深层语义理解；其次，对暗语仇恨言论的识别和解释研究不足，限制了模型在复杂真实场景中的可解释性。为解决这些问题，我们做出以下贡献：（1）我们引入了片段级目标感知毒性提取数据集（STATE ToxiCN），这是首个片段级中文仇恨言论数据集，并使用它评估了现有模型的仇恨语义理解能力。（2）我们首次对中文暗语仇恨词汇及LLMs解释仇恨语义的能力进行了全面研究。（3）我们提出了一种将标注词典整合到模型中的方法，显著提升了仇恨言论检测性能。我们的工作为推进中文仇恨言论检测研究的可解释性提供了宝贵资源与见解。
英文摘要：
The proliferation of hate speech has inflicted significant societal harm, with its intensity and directionality closely tied to specific targets and arguments. In recent years, numerous machine learning-based methods have been developed to detect hateful comments on online platforms automatically. However, research on Chinese hate speech detection lags behind, and interpretability studies face two major challenges: first, the scarcity of span-level fine-grained annotated datasets limits models' deep semantic understanding of hate speech; second, insufficient research on identifying and interpreting coded hate speech restricts model explainability in complex real-world scenarios. To address these, we make the following contributions: (1) We introduce the Span-level Target-Aware Toxicity Extraction dataset (STATE ToxiCN), the first span-level Chinese hate speech dataset, and evaluate the hate semantic understanding of existing models using it. (2) We conduct the first comprehensive study on Chinese coded hate terms, LLMs' ability to interpret hate semantics. (3) We propose a method to integrate an annotated lexicon into models, significantly enhancing hate speech detection performance. Our work provides valuable resources and insights to advance the interpretability of Chinese hate speech detection research.
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。
