研究人员开发了ImpSH，一个旨在提高隐式仇恨言论检测模型泛化能力的新框架。这种基于三元组的方法将帖子与其隐含陈述对齐，并使用上下文约束半硬负例来更好地区分相似但不同的仇恨言论实例。使用BERT和HateBERT模型在多个数据集上的评估表明，与标准的监督对比方法相比，ImpSH可以提高跨域性能。 AI
影响 这项研究可能有助于开发更强大、更具可迁移性的模型，以检测微妙形式的有害在线内容。
排序理由 该集群包含一篇详细介绍特定NLP任务新方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.18852v1 Announce Type: cross Abstract: Classifying implicit hate speech remains a challenge, as intent is often masked through insinuation and context rather than explicit slurs. Prior supervised contrastive approaches improve in-domain detection but can overfit surfac…
Classifying implicit hate speech remains a challenge, as intent is often masked through insinuation and context rather than explicit slurs. Prior supervised contrastive approaches improve in-domain detection but can overfit surface cues and struggle to transfer across datasets. W…