发表机构
Everdian（Everdian）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将辱骂性语言识别定义为序列标注任务，联合提取辱骂片段与目标提及，在试点语料上对比句子级分类，证明其竞争力并提供局部化输出，同时指出边界恢复难点与未来方向。
AI 中文摘要
工业级内容审核必须在严格的延迟约束下处理海量消息流，然而大多数辱骂性语言（AL）检测系统依赖于句子级分类（ALC），这种方法既无法定位辱骂性片段，也无法识别被针对的目标。我们将辱骂性语言识别（ALI）定义为一项序列标注任务，该任务联合提取AL片段和目标提及，并评估这种方法能否用于文本审核。在一个从生产审核流程中抽取的试点语料库上，我们将ALI与ALC在跨领域泛化和隐性辱骂方面进行比较，同时评估AL和目标片段检测。ALI在与ALC保持竞争力的同时，为审核人员提供局部化输出，在隐性辱骂方面具有适度且对配置敏感的优势。精确的AL边界和目标片段仍然难以恢复。我们通过定性分析补充了这一比较，并讨论了完整的目标-片段链接以及ALI结构化基准的前景。
英文摘要
Industrial content moderation must process massive message streams under tight latency constraints, yet most abusive language (AL) detection systems rely on sentence-level classification (ALC), which neither localizes abusive spans nor identifies who is targeted. We define Abusive Language Identification (ALI) as a sequence-labeling task that jointly extracts AL spans and target mentions, and assess whether this approach can be used for text moderation. On a pilot corpus drawn from a production moderation pipeline, we compare ALI with ALC on cross-domain generalization and implicit abuse, and we also evaluate AL and target span detection. ALI remains competitive with ALC while providing localized outputs for moderators, with a modest and configuration-sensitive advantage on implicit abuse. Exact AL boundaries and target spans remain difficult to recover. We complement this comparison with a qualitative analysis and discuss perspectives on complete target--span linking and on structured benchmarks for ALI.
