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    <abstract>Computational models of dialogue often struggle to capture the nuanced structures of spontaneous conversation - specifically in polyadic, real-world settings. We introduce a multilayered annotation protocol designed for the GaMMA corpus, a Danish dataset of four-person conversations recorded in both quiet and noisy environments. The protocol targets key interactional phenomena: Turn Construction Units, backchannels, floor transfer attempts, and repair sequences. Each annotation layer is grounded in Conversation Analysis while remaining machine-actionable, enabling alignment with multimodal data such as gaze and motion. We report inter-annotator agreement metrics across annotation tiers and discuss how the protocol supports both fine-grained interaction analysis and the training of context-aware dialogue models.</abstract>
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%0 Conference Proceedings
%T A Multi-Layered Annotation Protocol for Polyadic Conversation: Structuring Interactional Data in the GaMMA Corpus
%A Dourado, Mark
%A Lorenzen, Frej Spangsberg
%A Udesen, Jesper
%A Hassager, Henrik Gert
%A Serafin, Stefania
%Y Béchet, Frédéric
%Y Lefèvre, Fabrice
%Y Asher, Nicholas
%Y Kim, Seokhwan
%Y Merlin, Teva
%S Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2025
%8 August
%I Association for Computational Linguistics
%C Avignon, France
%F dourado-etal-2025-multi
%X Computational models of dialogue often struggle to capture the nuanced structures of spontaneous conversation - specifically in polyadic, real-world settings. We introduce a multilayered annotation protocol designed for the GaMMA corpus, a Danish dataset of four-person conversations recorded in both quiet and noisy environments. The protocol targets key interactional phenomena: Turn Construction Units, backchannels, floor transfer attempts, and repair sequences. Each annotation layer is grounded in Conversation Analysis while remaining machine-actionable, enabling alignment with multimodal data such as gaze and motion. We report inter-annotator agreement metrics across annotation tiers and discuss how the protocol supports both fine-grained interaction analysis and the training of context-aware dialogue models.
%U https://aclanthology.org/2025.sigdial-1.19/
%P 246-253

Markdown (Informal)

[A Multi-Layered Annotation Protocol for Polyadic Conversation: Structuring Interactional Data in the GaMMA Corpus](https://aclanthology.org/2025.sigdial-1.19/) (Dourado et al., SIGDIAL 2025)

• A Multi-Layered Annotation Protocol for Polyadic Conversation: Structuring Interactional Data in the GaMMA Corpus (Dourado et al., SIGDIAL 2025)

ACL

• Mark Dourado, Frej Spangsberg Lorenzen, Jesper Udesen, Henrik Gert Hassager, and Stefania Serafin. 2025. A Multi-Layered Annotation Protocol for Polyadic Conversation: Structuring Interactional Data in the GaMMA Corpus. In Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 246–253, Avignon, France. Association for Computational Linguistics.