LEDA: a Large-Organization Email-Based Decision-Dialogue-Act Analysis Dataset

Mladen Karan, Prashant Khare, Ravi Shekhar, Stephen McQuistin, Ignacio Castro, Gareth Tyson, Colin Perkins, Patrick Healey, Matthew Purver


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.
Anthology ID:
2023.findings-acl.378
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6080–6089
Language:
URL:
https://aclanthology.org/2023.findings-acl.378
DOI:
10.18653/v1/2023.findings-acl.378
Bibkey:
Cite (ACL):
Mladen Karan, Prashant Khare, Ravi Shekhar, Stephen McQuistin, Ignacio Castro, Gareth Tyson, Colin Perkins, Patrick Healey, and Matthew Purver. 2023. LEDA: a Large-Organization Email-Based Decision-Dialogue-Act Analysis Dataset. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6080–6089, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
LEDA: a Large-Organization Email-Based Decision-Dialogue-Act Analysis Dataset (Karan et al., Findings 2023)
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 https://preview.aclanthology.org/nschneid-patch-1/2023.findings-acl.378.mp4