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

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.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2608.28926 [cs.CL]

(or arXiv:2608.28926v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.28926 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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