构建首个多语言对话语境下的隐性毒性数据集，用于更精准识别网络暴力。
ToxiREX: A Dataset on Toxic REasoning in ConteXt
- 基于毒性推理框架，标注六种语言的评论上下文中的隐性攻击性
- 包含12.5万条训练数据与近3000条母语者标注测试数据，验证标注一致性
- 适合研究跨语言、上下文敏感的毒性检测，尤其关注隐性歧视与讽刺
我们提出ToxiREX：一个关于上下文中毒性推理的新数据集。该数据集包含来自六种语言（英语、阿拉伯语、土耳其语、西班牙语、德语、荷兰语）的Reddit评论线程，覆盖2023年土耳其地震、俄罗斯入侵乌克兰等重大事件。基于先前论文提出的系统性毒性推理框架，对评论的隐含意图进行结构化标注，支持映射现有毒性分类体系。数据集经上下文保留预处理，包含12.5万条由商业大模型标注的训练数据和近3000条由母语者标注的测试数据。分析显示，测试集中的表面分歧常源于合理不同解读而非噪声。我们通过提示与微调语言模型提供基线结果，并开发了针对层级式结构预测的评估策略。尽管模型表现优于随机，仍存在巨大提升空间，表明任务极具挑战性。ToxiREX是首个同时融合多语言、对话上下文与隐性毒性，并使用毒性推理框架进行丰富结构化标注的数据集。数据集已开源：https://github.com/cltl/toxirex
原文摘要 · Abstract (English)
We introduce a new, contextual, multilingual dataset called ToxiREX: Toxic REasoning in ConteXt. The dataset consists of threads of Reddit comments and structured characterizations of what the comments imply, following a systematic toxic reasoning schema developed in a previous paper. Using the schema allows us to capture and explain implicit and context-dependent toxicity, while supporting mappings to existing toxicity taxonomies. The dataset includes comments in six languages (English, Arabic, Turkish, Spanish, German, and Dutch), collected from posts connected to specific major events (e.g. the 2023 Turkey earthquakes; the Russian invasion of Ukraine). We describe the context-preserving preprocessing of the threads. We create a training set of 125 thousand comments which is annotated by a commercially available LLM, and a test set of just under three thousand comments that is annotated by native speakers. We show that apparent disagreements in the test set annotations often reflect defensible alternative interpretations rather than noise. Finally, we provide baseline results by prompting and fine-tuning language models. To produce these results, we develop evaluation strategies for our hierarchical, schema-based predictions. While models perform better than random, there remains a lot of room for improvement, showing the task to be challenging. ToxiREX is the first dataset to simultaneously incorporate multiple languages, conversational context, and implicit toxicity, while using the toxic reasoning schema for rich, structured annotations. Dataset available at: https://github.com/cltl/toxirex
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