Leveraging Turn-taking Dynamics for Intent Recognition in Multi-party Conversations
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 usef...
| Authors | Galo Castillo-López, Alexis Lombard, Gaël de Chalendar et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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