LEDA: a Large-Organization Email-based Decision-dialogue-Act analysis dataset

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

doi:10.18653/v1/2023.findings-acl.378

LEDA: a Large-Organization Email-based Decision-dialogue-Act analysis dataset

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

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Collaboration increasingly happens online. This is especially true for large groups working on global tasks, with collaborators all around the world. The size and distributed nature of such groups make 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 organization – 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 organizations. Finally, we experiment with a preliminary transformer-based dialog act tagging model.

Original language	English
Title of host publication	Findings of the association for computational linguistics
Subtitle of host publication	ACL 2023
Editors	Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Publisher	Association for Computational Linguistics (ACL)
Pages	6080–6089
DOIs	https://doi.org/10.18653/v1/2023.findings-acl.378
Publication status	Published - 9 Jul 2023
Event	Findings of the Association for Computational Linguistics - Toronto, Canada Duration: 9 Jul 2023 → 14 Jul 2023 https://aclanthology.org/2023.findings-acl

Conference

Conference	Findings of the Association for Computational Linguistics
Abbreviated title	ACL
Country/Territory	Canada
City	Toronto
Period	9/07/23 → 14/07/23
Internet address	https://aclanthology.org/2023.findings-acl

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10.18653/v1/2023.findings-acl.378Licence: CC BY

https://eprints.gla.ac.uk/297748/Licence: CC BY

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Karan, M., Khare, P., Shekhar, R., McQuistin, S., Perkins, C., Castro, I., Tyson, G., Healey, P., & Purver, M. (2023). LEDA: a Large-Organization Email-based Decision-dialogue-Act analysis dataset. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Findings of the association for computational linguistics: ACL 2023 (pp. 6080–6089). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.findings-acl.378

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abstract = "Collaboration increasingly happens online. This is especially true for large groups working on global tasks, with collaborators all around the world. The size and distributed nature of such groups make 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 organization – 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 organizations. Finally, we experiment with a preliminary transformer-based dialog act tagging model.",

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

note = "Funding: This work was supported by the UK EPSRC under grants EP/S033564/1 and EP/S036075/1 (Sodestream: Streamlining Social Decision Making for Enhanced Internet Standards). Purver was also supported by the Slovenian Research Agency via research core funding for the programme Knowledge Technologies (P2-0103).; Findings of the Association for Computational Linguistics, ACL ; Conference date: 09-07-2023 Through 14-07-2023",

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editor = "Anna Rogers and Jordan Boyd-Graber and Naoaki Okazaki",

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Karan, M, Khare, P, Shekhar, R, McQuistin, S, Perkins, C, Castro, I, Tyson, G, Healey, P & Purver, M 2023, LEDA: a Large-Organization Email-based Decision-dialogue-Act analysis dataset. in A Rogers, J Boyd-Graber & N Okazaki (eds), Findings of the association for computational linguistics: ACL 2023. Association for Computational Linguistics (ACL), pp. 6080–6089, Findings of the Association for Computational Linguistics, Toronto, Canada, 9/07/23. https://doi.org/10.18653/v1/2023.findings-acl.378

LEDA: a Large-Organization Email-based Decision-dialogue-Act analysis dataset. / Karan, Mladen; Khare, Prashant; Shekhar, Ravi et al.
Findings of the association for computational linguistics: ACL 2023. ed. / Anna Rogers; Jordan Boyd-Graber; Naoaki Okazaki. Association for Computational Linguistics (ACL), 2023. p. 6080–6089.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Perkins, Colin

AU - Castro, Ignacio

AU - Tyson, Gareth

AU - Healey, Patrick

AU - Purver, Matthew

N1 - Funding: This work was supported by the UK EPSRC under grants EP/S033564/1 and EP/S036075/1 (Sodestream: Streamlining Social Decision Making for Enhanced Internet Standards). Purver was also supported by the Slovenian Research Agency via research core funding for the programme Knowledge Technologies (P2-0103).

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N2 - Collaboration increasingly happens online. This is especially true for large groups working on global tasks, with collaborators all around the world. The size and distributed nature of such groups make 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 organization – 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 organizations. Finally, we experiment with a preliminary transformer-based dialog act tagging model.

AB - Collaboration increasingly happens online. This is especially true for large groups working on global tasks, with collaborators all around the world. The size and distributed nature of such groups make 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 organization – 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 organizations. Finally, we experiment with a preliminary transformer-based dialog act tagging model.

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Karan M, Khare P, Shekhar R, McQuistin S, Perkins C, Castro I et al. LEDA: a Large-Organization Email-based Decision-dialogue-Act analysis dataset. In Rogers A, Boyd-Graber J, Okazaki N, editors, Findings of the association for computational linguistics: ACL 2023. Association for Computational Linguistics (ACL). 2023. p. 6080–6089 doi: 10.18653/v1/2023.findings-acl.378

LEDA: a Large-Organization Email-based Decision-dialogue-Act analysis dataset

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