VivesDebate-Speech:  A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

Ramon Ruiz-Dolz; Javier Iranzo-Sánchez

doi:10.18653/v1/2023.emnlp-main.128

VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

Ramon Ruiz-Dolz, Javier Iranzo-Sánchez

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

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Abstract

In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks. The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly available resources in this topic. Moreover, we have performed a set of first-of-their-kind experiments which show an improvement when integrating audio features into the argument mining pipeline. The provided results can be used as a baseline for future research.

Original language	English
Title of host publication	Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Editors	Houda Bouamor, Juan Pino, Kalika Bali
Place of Publication	Singapore
Publisher	Association for Computational Linguistics (ACL)
Pages	2071-2077
Number of pages	7
ISBN (Print)	9798891760608
DOIs	https://doi.org/10.18653/v1/2023.emnlp-main.128
Publication status	Published - Dec 2023
Event	2023 Conference on Empirical Methods in Natural Language Processing - Hybrid, Singapore Duration: 6 Dec 2023 → 10 Dec 2023 https://2023.emnlp.org/

Conference

Conference	2023 Conference on Empirical Methods in Natural Language Processing
Abbreviated title	EMNLP 2023
Country/Territory	Singapore
Period	6/12/23 → 10/12/23
Internet address	https://2023.emnlp.org/

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10.18653/v1/2023.emnlp-main.128Licence: CC BY

Final published versionFinal published version, 172 KBLicence: CC BY

1 Conference contribution

Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks
Ruiz-Dolz, R., Heras, S. (Lead / Corresponding author) & García-Fornes, A., 2023, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Bouamor, H., Pino, J. & Bali, K. (eds.). Singapore: Association for Computational Linguistics (ACL), p. 6030-6040 11 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Ruiz-Dolz, R., & Iranzo-Sánchez, J. (2023). VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining. In H. Bouamor, J. Pino, & K. Bali (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 2071-2077). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.emnlp-main.128

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title = "VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining",

abstract = "In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks. The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly available resources in this topic. Moreover, we have performed a set of first-of-their-kind experiments which show an improvement when integrating audio features into the argument mining pipeline. The provided results can be used as a baseline for future research.",

author = "Ramon Ruiz-Dolz and Javier Iranzo-S{\'a}nchez",

year = "2023",

month = dec,

doi = "10.18653/v1/2023.emnlp-main.128",

language = "English",

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editor = "Houda Bouamor and Juan Pino and Kalika Bali",

booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",

publisher = "Association for Computational Linguistics (ACL)",

note = "2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 ; Conference date: 06-12-2023 Through 10-12-2023",

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}

Ruiz-Dolz, R & Iranzo-Sánchez, J 2023, VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining. in H Bouamor, J Pino & K Bali (eds), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), Singapore, pp. 2071-2077, 2023 Conference on Empirical Methods in Natural Language Processing, Singapore, 6/12/23. https://doi.org/10.18653/v1/2023.emnlp-main.128

VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining. / Ruiz-Dolz, Ramon; Iranzo-Sánchez, Javier.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. ed. / Houda Bouamor; Juan Pino; Kalika Bali. Singapore: Association for Computational Linguistics (ACL), 2023. p. 2071-2077.

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

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AB - In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks. The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly available resources in this topic. Moreover, we have performed a set of first-of-their-kind experiments which show an improvement when integrating audio features into the argument mining pipeline. The provided results can be used as a baseline for future research.

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Ruiz-Dolz R, Iranzo-Sánchez J. VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining. In Bouamor H, Pino J, Bali K, editors, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Singapore: Association for Computational Linguistics (ACL). 2023. p. 2071-2077 doi: 10.18653/v1/2023.emnlp-main.128

VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

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Research output

Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks

Cite this