@inproceedings{fukushige-etal-2026-estimating,
    title = "Estimating Relationships between Participants in Multi-Party Chat Corpus",
    author = "Fukushige, Akane  and
      Inoue, Koji  and
      Ochi, Keiko  and
      Kawahara, Tatsuya  and
      Yamashita, Sanae  and
      Higashinaka, Ryuichiro",
    editor = "Riccardi, Giuseppe  and
      Mousavi, Seyed Mahed  and
      Torres, Maria Ines  and
      Yoshino, Koichiro  and
      Callejas, Zoraida  and
      Chowdhury, Shammur Absar  and
      Chen, Yun-Nung  and
      Bechet, Frederic  and
      Gustafson, Joakim  and
      Damnati, G{\'e}raldine  and
      Papangelis, Alex  and
      D{'}Haro, Luis Fernando  and
      Mendon{\c{c}}a, John  and
      Bernardi, Raffaella  and
      Hakkani-Tur, Dilek  and
      Di Fabbrizio, Giuseppe {''}Pino{''}  and
      Kawahara, Tatsuya  and
      Alam, Firoj  and
      Tur, Gokhan  and
      Johnston, Michael",
    booktitle = "Proceedings of the 16th International Workshop on Spoken Dialogue System Technology",
    month = feb,
    year = "2026",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard-stats/2026.iwsds-1.38/",
    pages = "379--390",
    abstract = "While most existing dialogue studies focus on dyadic (one-on-one) interactions, research on multi-party dialogues has gained increasing importance. One key challenge in multi-party dialogues is identifying and interpreting the relationships between participants. This study focuses on multi-party chat corpus and aims to estimate participant pairs with specific relationships, such as family and acquaintances. We evaluated the performance of large language models ({LLM}s) in estimating these relationships, comparing them with a logistic regression model that uses interpretable textual features, including the number of turns and the frequency of honorific expressions. The results show that even advanced {LLM}s struggle with social relationship estimation, performing worse than a simple heuristic-based approach. This finding highlights the need for further improvement in enabling {LLM}s to naturally capture social relationships in multi-party dialogues."
}