@inproceedings{bhattacharya-di-eugenio-2026-clarvis,
    title = "{C}lar{V}is: A Dataset of Clarification Requests and Grounding in Collaborative Data Visualization Dialogues",
    author = "Bhattacharya, Abari  and
      Di Eugenio, Barbara",
    editor = "Choi, Jinho D.  and
      Chen, Yun-Nung  and
      Funakoshi, Kotaro  and
      Emami, Ali",
    booktitle = "Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = aug,
    year = "2026",
    address = "Atlanta, Georgia, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/revision-workflow/2026.sigdial-1.6/",
    pages = "79--90",
    abstract = "Clarification Requests (CRs) play a crucial role in human communication. However, existing datasets are often limited to single-turn clarifications or simulated tasks. We present a novel task-oriented dialogue dataset, ClarVis, of CRs collected from real-time multi-user collaborative data-exploration sessions, where users analyze data together while interacting with a visualization-generating conversational assistant. This dataset fills important gaps in current CR datasets by identifying naturally occurring CRs grounded by real-world modalities like hearing, vision, and actions in the physical environment. Our dataset includes 6.3K utterances across collaborative tasks, where each CR is annotated with a grounding modality - Auditory (A), Visual (V), or Kinesthetic (K) - that captures the context the clarification pertains to. Further, we establish benchmark tasks for CR identification and grounding-modality classification, and evaluate them with traditional machine learning models as well as instruction-tuned large language models. The results highlight both the learnability and the difficulty of these tasks, and position ClarVis as a useful resource for studying clarifications in collaborative dialogue settings."
}