@inproceedings{cai-etal-2025-search,
    title = "In Search of the Lost Arch in Dialogue: A Dependency Dialogue Acts Corpus for Multi-Party Dialogues",
    author = "Cai, Jon  and
      King, Brendan  and
      Cameron, Peyton  and
      Brown, Susan Windisch  and
      Eckert, Miriam  and
      Srinivas, Dananjay  and
      Baker, George Arthur  and
      Everson, V Kate  and
      Palmer, Martha  and
      Martin, James  and
      Flanigan, Jeffrey",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.1032/",
    doi = "10.18653/v1/2025.findings-acl.1032",
    pages = "20135--20149",
    ISBN = "979-8-89176-256-5",
    abstract = "Understanding the structure of multi-party conversation and the intentions and dialogue acts of each speaker remains a significant challenge in NLP. While a number of corpora annotated using theoretical frameworks of dialogue have been proposed, these typically focus on either utterance-level labeling of speaker intent, missing wider context, or the rhetorical structure of a dialogue, losing fine-grained intents captured in dialogue acts. Recently, the Dependency Dialogue Acts (DDA) framework has been proposed to for modeling both the fine-grained intents of each speaker and the structure of multi-party dialogues. However, there is not yet a corpus annotated with this framework available for the community to study. To address this gap, we introduce a new corpus of 33 dialogues and over 9,000 utterance units, densely annotated using the Dependency Dialogue Acts (DDA) framework.Our dataset spans four genres of multi-party conversations from different modalities: (1) physics classroom discussions, (2) engineering classroom discussions, (3) board game interactions, and (4) written online game chat logs. Each session is doubly annotated and adjudicated to ensure high-quality labeling. We present a description of the dataset and annotation process, an analysis of speaker dynamics enabled by our annotation, and a baseline evaluation of LLMs as DDA parsers. We discuss the implications of this dataset understanding dynamics between speakers and for developing more controllable dialogue agents."
}