@article{wagner-etal-2026-mudic,
    title = "{MUD}i{C}: A Dataset for Multi-User Dialogue and Collaboration in Chatbot Interaction",
    author = "Wagner, Nicolas  and
      Luna Jimenez, Cristina  and
      Andre, Elisabeth  and
      Minker, Wolfgang  and
      Ultes, Stefan",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.151/",
    pages = "1925--1933",
    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."
}