@inproceedings{castillo-lopez-etal-2026-leveraging,
title = "Leveraging Turn-taking Dynamics for Intent Recognition in Multi-party Conversations",
author = {Castillo-L{\'o}pez, Galo and
Lombard, Alexis and
de Chalendar, Ga{\"e}l and
Semmar, Nasredine},
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.160/",
pages = "2717--2731",
ISBN = "979-8-89176-460-6",
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."
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%0 Conference Proceedings
%T Leveraging Turn-taking Dynamics for Intent Recognition in Multi-party Conversations
%A Castillo-López, Galo
%A Lombard, Alexis
%A de Chalendar, Gaël
%A Semmar, Nasredine
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F castillo-lopez-etal-2026-leveraging
%X 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.
%U https://aclanthology.org/2026.emnlp-industry.160/
%P 2717-2731
Markdown (Informal)
[Leveraging Turn-taking Dynamics for Intent Recognition in Multi-party Conversations](https://aclanthology.org/2026.emnlp-industry.160/) (Castillo-López et al., EMNLP 2026)
ACL