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M-Arg: multimodal argument mining dataset for political debates with audio and transcripts

M-Arg: multimodal argument mining dataset for political debates with audio and transcripts
M-Arg: multimodal argument mining dataset for political debates with audio and transcripts
Argumentation mining aims at extracting, analysing and modelling people’s arguments, but large, high-quality annotated datasets are limited, and no multimodal datasets exist for this task. In this paper, we present M-Arg, a multimodal argument mining dataset with a corpus of US 2020 presidential debates, annotated through crowd-sourced annotations. This dataset allows models to be trained to extract arguments from natural dialogue such as debates using information like the intonation and rhythm of the speaker. Our dataset contains 7 hours of annotated US presidential debates, 6527 utterances and 4104 relation labels, and we report results from different baseline models with highest accuracy of 0.86 with a multimodal model.
Natural Language Processing, Argument Mining, Artificial Intelligence
Mestre, Rafael
33721a01-ab1a-4f71-8b0e-abef8afc92f3
Milicin, Razvan
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Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Ryan, Matthew
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Zhu, Jiatong
52569115-5d72-4fc0-8876-a66b991ed209
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Mestre, Rafael
33721a01-ab1a-4f71-8b0e-abef8afc92f3
Milicin, Razvan
bcc0599d-114a-4cd0-8fc9-202421b69caa
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Ryan, Matthew
f07cd3e8-f3d9-4681-9091-84c2df07cd54
Zhu, Jiatong
52569115-5d72-4fc0-8876-a66b991ed209
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d

Mestre, Rafael, Milicin, Razvan, Middleton, Stuart, Ryan, Matthew, Zhu, Jiatong and Norman, Timothy (2021) M-Arg: multimodal argument mining dataset for political debates with audio and transcripts. 8th Workshop on Argument Mining. 9 pp . (doi:10.18653/v1/2021.argmining-1.8).

Record type: Conference or Workshop Item (Paper)

Abstract

Argumentation mining aims at extracting, analysing and modelling people’s arguments, but large, high-quality annotated datasets are limited, and no multimodal datasets exist for this task. In this paper, we present M-Arg, a multimodal argument mining dataset with a corpus of US 2020 presidential debates, annotated through crowd-sourced annotations. This dataset allows models to be trained to extract arguments from natural dialogue such as debates using information like the intonation and rhythm of the speaker. Our dataset contains 7 hours of annotated US presidential debates, 6527 utterances and 4104 relation labels, and we report results from different baseline models with highest accuracy of 0.86 with a multimodal model.

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ArgMin_Workshop_Paper - Accepted Manuscript
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Accepted/In Press date: 1 September 2021
Published date: November 2021
Venue - Dates: 8th Workshop on Argument Mining, 2021-11-01
Keywords: Natural Language Processing, Argument Mining, Artificial Intelligence

Identifiers

Local EPrints ID: 452873
URI: http://eprints.soton.ac.uk/id/eprint/452873
PURE UUID: 86bb4e31-ee54-47b7-99ff-90259049a07a
ORCID for Rafael Mestre: ORCID iD orcid.org/0000-0002-2460-4234
ORCID for Stuart Middleton: ORCID iD orcid.org/0000-0001-8305-8176
ORCID for Matthew Ryan: ORCID iD orcid.org/0000-0002-8693-5063
ORCID for Timothy Norman: ORCID iD orcid.org/0000-0002-6387-4034

Catalogue record

Date deposited: 06 Jan 2022 17:37
Last modified: 09 Jan 2022 04:12

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Contributors

Author: Rafael Mestre ORCID iD
Author: Razvan Milicin
Author: Matthew Ryan ORCID iD
Author: Jiatong Zhu
Author: Timothy Norman ORCID iD

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