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    <abstract>We introduce MUDiC, a novel dataset on task-based multi-user interactions in chatbots. Unlike most traditional dialogue corpora that focus on one-to-one human–chatbot exchanges, this dataset captures conversations involving two human participants engaging with a single system. The data include diverse conversational contexts such as shared group task, user intents, and mechanisms to deal with off-topic talk. MUDiC consists of 1,689 dialogue exchanges between 20 groups and the chatbot. Each session is annotated with user id, interaction turns, and intents and dialogue acts, enabling an analysis of group conversational dynamics. Consequently, the dataset aims to support tasks such as multi-user dialogue modelling, intent disambiguation, and moderation behaviour, which are relevant factors for the design of socially aware chatbots.</abstract>
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%0 Conference Proceedings
%T MUDiC: A Dataset for Multi-User Dialogue and Collaboration in Chatbot Interaction
%A Wagner, Nicolas
%A Luna Jimenez, Cristina
%A Andre, Elisabeth
%A Minker, Wolfgang
%A Ultes, Stefan
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F wagner-etal-2026-mudic
%X We introduce MUDiC, a novel dataset on task-based multi-user interactions in chatbots. Unlike most traditional dialogue corpora that focus on one-to-one human–chatbot exchanges, this dataset captures conversations involving two human participants engaging with a single system. The data include diverse conversational contexts such as shared group task, user intents, and mechanisms to deal with off-topic talk. MUDiC consists of 1,689 dialogue exchanges between 20 groups and the chatbot. Each session is annotated with user id, interaction turns, and intents and dialogue acts, enabling an analysis of group conversational dynamics. Consequently, the dataset aims to support tasks such as multi-user dialogue modelling, intent disambiguation, and moderation behaviour, which are relevant factors for the design of socially aware chatbots.
%R 10.63317/36zqrpocuvn9
%U https://aclanthology.org/2026.lrec-1.151/
%U https://doi.org/10.63317/36zqrpocuvn9
%P 1925-1933

Markdown (Informal)

[MUDiC: A Dataset for Multi-User Dialogue and Collaboration in Chatbot Interaction](https://aclanthology.org/2026.lrec-1.151/) (Wagner et al., LREC 2026)

• MUDiC: A Dataset for Multi-User Dialogue and Collaboration in Chatbot Interaction (Wagner et al., LREC 2026)

ACL

• Nicolas Wagner, Cristina Luna Jimenez, Elisabeth Andre, Wolfgang Minker, and Stefan Ultes. 2026. MUDiC: A Dataset for Multi-User Dialogue and Collaboration in Chatbot Interaction. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 1925–1933, Palma de Mallorca, Spain. ELRA Language Resource Association.