Problem
Research questions and friction points this paper is trying to address.
Low-Data
Cross-Domain
Conversation Derailment
Leveraging Speech Acts for Low-Data and Cross-Domain Conversation Derailment Forecasting
🤖 AI Summary
该研究通过结合言语行为信息和文本语义来预测在线讨论中的敌对升级，以解决低数据环境和跨领域泛化的问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Speech Acts
Low-Data
Cross-Domain
Conversation Derailment
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