Leveraging Turn-taking Dynamics for Intent Recognition in Multi-party Conversations

2026-08-28

AI Summary

研究通过引入衡量对话转换可预测性的自监督目标，采用多任务学习方法改进多人对话中的意图识别问题。

Abstract

We propose a multi-task learning approach for multi-party dialogue intent recognition that leverages an auxiliary task that models turn-taking dynamics. Specifically, we introduce turn-transition entropy, a self-supervised target computed from the sequence of speaker transitions, which quantifies the predictability of interaction patterns. Experiments on multiple pre-trained models demonstrate that incorporating this auxiliary task improves intent recognition performance, outperforming existing approaches which ignore multi-party interaction dynamics. We find that our proposed continuous target can be learned as a single-task objective, suggesting that it is an actual signal carrying useful information.

Problem

• multi-party conversations
• intent recognition
• turn-taking dynamics

Innovation

• multi-task learning
• turn-taking dynamics
• turn-transition entropy
• intent recognition