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    <abstract>In conversation, humans use multimodal cues, such as speech, gestures, and gaze, to manage turn-taking. While linguistic and acoustic features are informative, gestures provide complementary cues for modeling these transitions. To study this, we introduce DnD Gesture++, an extension of the multi-party DnD Gesture corpus enriched with 2,663 semantic gesture annotations spanning iconic, metaphoric, deictic, and discourse types. Using this dataset, we model turn-taking prediction through a Mixture-of-Experts framework integrating text, audio, and gestures. Experiments show that incorporating semantically guided gestures yields consistent performance gains over baselines, demonstrating their complementary role in multimodal turn-taking.</abstract>
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%0 Conference Proceedings
%T Modeling Turn-Taking with Semantically Informed Gestures
%A Suresh, Varsha
%A Mughal, M. Hamza
%A Theobalt, Christian
%A Demberg, Vera
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F suresh-etal-2026-modeling
%X In conversation, humans use multimodal cues, such as speech, gestures, and gaze, to manage turn-taking. While linguistic and acoustic features are informative, gestures provide complementary cues for modeling these transitions. To study this, we introduce DnD Gesture++, an extension of the multi-party DnD Gesture corpus enriched with 2,663 semantic gesture annotations spanning iconic, metaphoric, deictic, and discourse types. Using this dataset, we model turn-taking prediction through a Mixture-of-Experts framework integrating text, audio, and gestures. Experiments show that incorporating semantically guided gestures yields consistent performance gains over baselines, demonstrating their complementary role in multimodal turn-taking.
%U https://aclanthology.org/2026.findings-eacl.106/
%P 2034-2041

Markdown (Informal)

[Modeling Turn-Taking with Semantically Informed Gestures](https://aclanthology.org/2026.findings-eacl.106/) (Suresh et al., Findings 2026)

• Modeling Turn-Taking with Semantically Informed Gestures (Suresh et al., Findings 2026)

ACL

• Varsha Suresh, M. Hamza Mughal, Christian Theobalt, and Vera Demberg. 2026. Modeling Turn-Taking with Semantically Informed Gestures. In Findings of the Association for Computational Linguistics: EACL 2026, pages 2034–2041, Rabat, Morocco. Association for Computational Linguistics.