@article{ezure-inaba-2026-storyccdial,
    title = "{S}tory{CCD}ial: Collecting and Analyzing Human-Human Co-Creation Dialogues for Personalized Creative Support",
    author = "Ezure, Natsumi  and
      Inaba, Michimasa",
    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.152/",
    pages = "1934--1946",
    abstract = "With the development of generative models, research on human-AI co-creation has been actively conducted. However, in the field of co-creation, research on system personalization according to individual characteristics is insufficient, and little focus has been placed on individual differences in creation. Therefore, in this study, we constructed StoryCCDial, a co-creation dialogue dataset aimed at the personalization of co-creative dialogue systems. First, we collected human-human story co-creation dialogue data involving 120 workers and constructed a dataset that includes dialogues, dialogue acts, the workers' personality traits, postsurveys, and edit histories from the interface. Next, using the constructed dataset, we conducted analyses focusing on the workers' personality traits, the number of utterances, and edit histories. The analysis revealed differences in dialogue content based on workers' personality traits, individual differences in the number of utterances during the co-creation process, and variations in creative workflows on the interface. Our dataset will be available at https://github.com/UEC-InabaLab/StoryCCDial ."
}