@inproceedings{karan-etal-2023-leda,
    title = "{LEDA}: a Large-Organization Email-Based Decision-Dialogue-Act Analysis Dataset",
    author = "Karan, Mladen  and
      Khare, Prashant  and
      Shekhar, Ravi  and
      McQuistin, Stephen  and
      Castro, Ignacio  and
      Tyson, Gareth  and
      Perkins, Colin  and
      Healey, Patrick  and
      Purver, Matthew",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.378",
    doi = "10.18653/v1/2023.findings-acl.378",
    pages = "6080--6089",
    abstract = "Collaboration increasingly happens online. This is especially true for large groups working on global tasks, with collaborators all around the globe. The size and distributed nature of such groups makes decision-making challenging. This paper proposes a set of dialog acts for the study of decision-making mechanisms in such groups, and provides a new annotated dataset based on real-world data from the public mail-archives of one such organisation {--} the Internet Engineering Task Force (IETF). We provide an initial data analysis showing that this dataset can be used to better understand decision-making in such organisations. Finally, we experiment with a preliminary transformer-based dialog act tagging model.",
}

Collaboration increasingly happens online. This is especially true for large groups working on global tasks, with collaborators all around the globe. The size and distributed nature of such groups makes decision-making challenging. This paper proposes a set of dialog acts for the study of decision-making mechanisms in such groups, and provides a new annotated dataset based on real-world data from the public mail-archives of one such organisation {--} the Internet Engineering Task Force (IETF). We provide an initial data analysis showing that this dataset can be used to better understand decision-making in such organisations. Finally, we experiment with a preliminary transformer-based dialog act tagging model.
Karan, Mladen Khare, Prashant Shekhar, Ravi McQuistin, Stephen Castro, Ignacio Tyson, Gareth Perkins, Colin Healey, Patrick Purver, Matthew
LEDA: a Large-Organization Email-Based Decision-Dialogue-Act Analysis Dataset