用对话行为分析预测网络争吵，小数据下也更准。
Leveraging Speech Acts for Low-Data and Cross-Domain Conversation Derailment Forecasting
- 结合话语行为与文本语义，降低语言噪声干扰。
- 在三个数据集上表现更好，低数据和跨领域场景提升显著。
- 适合新平台或小社区的实时争议预警系统。
对话脱轨预测旨在提前判断在线讨论是否会升级为敌意，从而实现主动干预。现有方法在数据稀缺和跨领域场景中表现不佳，尤其对新平台和小型社区构成挑战。本文通过建模对话的语用表征来减少词汇噪声，提升泛化能力。具体而言，将话语行为信息作为辅助信号，与文本语义联合学习。实验结果表明，在三个数据集上性能均有提升，尤其在低数据和跨领域设置中表现突出。
原文摘要 · Abstract (English)
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.
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