发表机构
The University of Melbourne（墨尔本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对对话偏离预测在低数据与跨领域场景下的不足，提出结合言语行为与文本语义的辅助学习方法，在三个数据集上取得了性能提升。
AI 中文摘要
对话偏离预测旨在预判在线讨论何时会升级为敌对状态，从而实现主动 moderation（ moderation 可译为 moderation 或内容审核）。现有方法在低数据场景和跨领域泛化方面常存在不足，这给标注数据有限的新平台和小型社区带来了挑战。我们提出对对话的语用表征进行建模，以减少词汇噪声并提升泛化能力，具体而言，将言语行为信息作为辅助学习信号与文本语义共同使用。实验结果显示，在三个数据集上的性能均有所提升，尤其在低数据和跨领域场景中表现突出。
英文摘要
Conversational derailment forecasting aims to predict when online discussions will escalate into hostility, enabling proactive moderation. Existing approaches often struggle in low-data settings and to generalize across domains. This poses a challenge for new platforms and smaller communities where annotated data is limited. We propose modeling pragmatic representations of conversations to reduce lexical noise and improve generalizability. Specifically, speech act information is used as an auxiliary learning signal alongside textual semantics. Experimental results show improved performance across three datasets, particularly in low-data and cross-domain settings.
