M-Arg: Multimodal Argument Mining Dataset for Political Debates with Audio and Transcripts

Rafael Mestre, Razvan Milicin, Stuart Middleton, Matt Ryan, Jiatong Zhu, Timothy J. Norman


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, namely a text-only model, an audio-only model and multimodal models that extract features from both text and audio. With accuracy reaching 0.86 in multimodal models, we find that audio features provide added value with respect to text-only models.
Anthology ID:
2021.argmining-1.8
Volume:
Proceedings of the 8th Workshop on Argument Mining
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
ArgMining | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
78–88
Language:
URL:
https://aclanthology.org/2021.argmining-1.8
DOI:
10.18653/v1/2021.argmining-1.8
Bibkey:
Cite (ACL):
Rafael Mestre, Razvan Milicin, Stuart Middleton, Matt Ryan, Jiatong Zhu, and Timothy J. Norman. 2021. M-Arg: Multimodal Argument Mining Dataset for Political Debates with Audio and Transcripts. In Proceedings of the 8th Workshop on Argument Mining, pages 78–88, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
M-Arg: Multimodal Argument Mining Dataset for Political Debates with Audio and Transcripts (Mestre et al., ArgMining 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.argmining-1.8.pdf
Code
 rafamestre/m-arg_multimodal-argumentation-dataset