Graph-based Argument Quality Assessment

Ekaterina Saveleva, Volha Petukhova, Marius Mosbach, Dietrich Klakow


Abstract
The paper presents a novel discourse-based approach to argument quality assessment defined as a graph classification task, where the depth of reasoning (argumentation) is evident from the number and type of detected discourse units and relations between them. We successfully applied state-of-the-art discourse parsers and machine learning models to reconstruct argument graphs with the identified and classified discourse units as nodes and relations between them as edges. Then Graph Neural Networks were trained to predict the argument quality assessing its acceptability, relevance, sufficiency and overall cogency. The obtained accuracy ranges from 74.5% to 85.0% and indicates that discourse-based argument structures reflect qualitative properties of natural language arguments. The results open many interesting prospects for future research in the field of argumentation mining.
Anthology ID:
2021.ranlp-1.143
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1268–1280
Language:
URL:
https://aclanthology.org/2021.ranlp-1.143
DOI:
Bibkey:
Cite (ACL):
Ekaterina Saveleva, Volha Petukhova, Marius Mosbach, and Dietrich Klakow. 2021. Graph-based Argument Quality Assessment. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1268–1280, Held Online. INCOMA Ltd..
Cite (Informal):
Graph-based Argument Quality Assessment (Saveleva et al., RANLP 2021)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2021.ranlp-1.143.pdf