@inproceedings{khebour-etal-2024-common,
    title = "Common Ground Tracking in Multimodal Dialogue",
    author = "Khebour, Ibrahim Khalil  and
      Lai, Kenneth  and
      Bradford, Mariah  and
      Zhu, Yifan  and
      Brutti, Richard A.  and
      Tam, Christopher  and
      Tu, Jingxuan  and
      Ibarra, Benjamin A.  and
      Blanchard, Nathaniel  and
      Krishnaswamy, Nikhil  and
      Pustejovsky, James",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.318",
    pages = "3587--3602",
    abstract = "Within Dialogue Modeling research in AI and NLP, considerable attention has been spent on {``}dialogue state tracking{''} (DST), which is the ability to update the representations of the speaker{'}s needs at each turn in the dialogue by taking into account the past dialogue moves and history. Less studied but just as important to dialogue modeling, however, is {``}common ground tracking{''} (CGT), which identifies the shared belief space held by all of the participants in a task-oriented dialogue: the task-relevant propositions all participants accept as true. In this paper we present a method for automatically identifying the current set of shared beliefs and {''}questions under discussion{''} (QUDs) of a group with a shared goal. We annotate a dataset of multimodal interactions in a shared physical space with speech transcriptions, prosodic features, gestures, actions, and facets of collaboration, and operationalize these features for use in a deep neural model to predict moves toward construction of common ground. Model outputs cascade into a set of formal closure rules derived from situated evidence and belief axioms and update operations. We empirically assess the contribution of each feature type toward successful construction of common ground relative to ground truth, establishing a benchmark in this novel, challenging task.",
}

Within Dialogue Modeling research in AI and NLP, considerable attention has been spent on {``}dialogue state tracking{''} (DST), which is the ability to update the representations of the speaker{'}s needs at each turn in the dialogue by taking into account the past dialogue moves and history. Less studied but just as important to dialogue modeling, however, is {``}common ground tracking{''} (CGT), which identifies the shared belief space held by all of the participants in a task-oriented dialogue: the task-relevant propositions all participants accept as true. In this paper we present a method for automatically identifying the current set of shared beliefs and {''}questions under discussion{''} (QUDs) of a group with a shared goal. We annotate a dataset of multimodal interactions in a shared physical space with speech transcriptions, prosodic features, gestures, actions, and facets of collaboration, and operationalize these features for use in a deep neural model to predict moves toward construction of common ground. Model outputs cascade into a set of formal closure rules derived from situated evidence and belief axioms and update operations. We empirically assess the contribution of each feature type toward successful construction of common ground relative to ground truth, establishing a benchmark in this novel, challenging task.
Khebour, Ibrahim Khalil Lai, Kenneth Bradford, Mariah Zhu, Yifan Brutti, Richard A. Tam, Christopher Tu, Jingxuan Ibarra, Benjamin A. Blanchard, Nathaniel Krishnaswamy, Nikhil Pustejovsky, James
Common Ground Tracking in Multimodal Dialogue