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

Ramon Ruiz-Dolz; Stella Heras; Ana García-Fornes

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

Ramon Ruiz-Dolz, Stella Heras (Lead / Corresponding author), Ana García-Fornes

Computing

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

1 Citation (Scopus)

37 Downloads (Pure)

Abstract

The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks. Such is the case of the automatic evaluation of complete professional argumentative debates. In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates. For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks. Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.

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	6030-6040
Number of pages	11
ISBN (Electronic)	9798891760608
Publication status	Published - 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/

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Information Systems
Linguistics and Language

Access to Document

Final published versionFinal published version, 293 KBLicence: CC BY

https://aclanthology.org/2023.emnlp-main.368.pdfLicence: CC BY

Cite this

Ruiz-Dolz, R., Heras, S., & García-Fornes, A. (2023). Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks. In H. Bouamor, J. Pino, & K. Bali (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 6030-6040). Association for Computational Linguistics (ACL). https://aclanthology.org/2023.emnlp-main.368.pdf

Ruiz-Dolz, Ramon ; Heras, Stella ; García-Fornes, Ana. / Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. editor / Houda Bouamor ; Juan Pino ; Kalika Bali. Singapore : Association for Computational Linguistics (ACL), 2023. pp. 6030-6040

@inproceedings{3ae212ba405444b1a3178b6e551866eb,

title = "Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks",

abstract = "The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks. Such is the case of the automatic evaluation of complete professional argumentative debates. In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates. For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks. Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.",

author = "Ramon Ruiz-Dolz and Stella Heras and Ana Garc{\'i}a-Fornes",

note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 ; Conference date: 06-12-2023 Through 10-12-2023",

year = "2023",

language = "English",

pages = "6030--6040",

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)",

url = "https://2023.emnlp.org/",

}

Ruiz-Dolz, R, Heras, S & García-Fornes, A 2023, Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks. 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. 6030-6040, 2023 Conference on Empirical Methods in Natural Language Processing, Singapore, 6/12/23. <https://aclanthology.org/2023.emnlp-main.368.pdf>

Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks. / Ruiz-Dolz, Ramon; Heras, Stella (Lead / Corresponding author); García-Fornes, Ana.
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. 6030-6040.

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

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T1 - Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks

AU - Ruiz-Dolz, Ramon

AU - Heras, Stella

AU - García-Fornes, Ana

PY - 2023

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N2 - The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks. Such is the case of the automatic evaluation of complete professional argumentative debates. In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates. For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks. Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.

AB - The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks. Such is the case of the automatic evaluation of complete professional argumentative debates. In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates. For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks. Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.

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M3 - Conference contribution

AN - SCOPUS:85184827144

SP - 6030

EP - 6040

BT - Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

A2 - Bouamor, Houda

A2 - Pino, Juan

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PB - Association for Computational Linguistics (ACL)

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T2 - 2023 Conference on Empirical Methods in Natural Language Processing

Y2 - 6 December 2023 through 10 December 2023

ER -

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

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