SEAHateCheck：用于检测东南亚低资源语言仇恨言论的功能测试
SEAHateCheck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of Southeast Asia
AI总结：
本文提出SEAHateCheck，首个针对东南亚四国语言的仇恨言论检测基准，通过功能测试框架和本地专家验证，揭示模型在低资源语言中检测的局限性，推动文化敏感的实用工具开发。
摘要：
仇恨言论检测严重依赖语言资源，这些资源主要集中在高资源语言如英语和中文，阻碍了东南亚低资源语言的研究和平台开发。为此，我们引入SEAHateCheck，首个针对印尼、泰国、菲律宾和越南的语言数据集，涵盖印尼语、塔加洛语、泰语和越南语。基于HateCheck的功能测试框架和SGHateCheck的方法，SEAHateCheck提供了文化相关的测试案例，通过大规模语言模型和本地专家验证准确性。实验显示，最先进的多语言模型在特定低资源语言中存在局限，尤其是塔加洛语测试案例的模型准确率最低，可能由于语言复杂性和训练数据有限。相比之下，基于俚语的功能测试最困难，因为模型难以处理文化细微表达。SEAHateCheck的诊断洞察进一步揭示了模型在隐性仇恨检测和反仇恨表达方面的弱点。作为首个针对这些东南亚语言的功能测试套件，本文为研究人员提供了稳健的基准，推动了实用、文化敏感的仇恨言论检测工具的开发，以促进包容性的在线内容管理。
英文摘要：
Hate speech detection relies heavily on linguistic resources, which are primarily available in high-resource languages such as English and Chinese, creating barriers for researchers and platforms developing tools for low-resource languages in Southeast Asia, where diverse socio-linguistic contexts complicate online hate moderation. To address this, we introduce SEAHateCheck, a pioneering dataset tailored to Indonesia, Thailand, the Philippines, and Vietnam, covering Indonesian, Tagalog, Thai, and Vietnamese. Building on HateCheck's functional testing framework and refining SGHateCheck's methods, SEAHateCheck provides culturally relevant test cases, augmented by large language models and validated by local experts for accuracy. Experiments with state-of-the-art and multilingual models revealed limitations in detecting hate speech in specific low-resource languages. In particular, Tagalog test cases showed the lowest model accuracy, likely due to linguistic complexity and limited training data. In contrast, slang-based functional tests proved the hardest, as models struggled with culturally nuanced expressions. The diagnostic insights of SEAHateCheck further exposed model weaknesses in implicit hate detection and models' struggles with counter-speech expression. As the first functional test suite for these Southeast Asian languages, this work equips researchers with a robust benchmark, advancing the development of practical, culturally attuned hate speech detection tools for inclusive online content moderation.
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。
