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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.findings-acl.378.pdf