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

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: Contribution to conference › Paper › peer-review

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
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

Cite this

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title = "LEDA: a Large-Organization Email-Based Decision-Dialogue-Act Analysis Dataset",

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",

year = "2023",

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day = "9",

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T1 - LEDA: a Large-Organization Email-Based Decision-Dialogue-Act Analysis Dataset

AU - Karan, Mladen

AU - Khare, Prashant

AU - Shekhar, Ravi

AU - McQuistin, Stephen

AU - Perkins, Colin

AU - Castro, Ignacio

AU - Tyson, Gareth

AU - Healey, Patrick

AU - Purver, Matthew

PY - 2023/7/9

Y1 - 2023/7/9

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.

M3 - Paper

T2 - Findings of the Association for Computational Linguistics

Y2 - 9 July 2023 through 14 July 2023

ER -

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

Abstract

Conference

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