Mining Complex Patterns of Argumentative Reasoning in Natural Language Dialogue

Ramon Ruiz-Dolz, Zlata Kikteva, John Lawrence


Abstract
Argumentation scheme mining is the task of automatically identifying reasoning mechanisms behind argument inferences. These mechanisms provide insights into underlying argument structures and guide the assessment of natural language arguments. Research on argumentation scheme mining, however, has always been limited by the scarcity of large enough publicly available corpora containing scheme annotations. In this paper, we present the first state-of-the-art results for mining argumentation schemes in natural language dialogue. For this purpose, we create QT-Schemes, a new corpus of 441 arguments annotated with 24 argumentation schemes. Using this corpus, we leverage the capabilities of LLMs and Transformer-based models, pre-training them on a large corpus containing textbook-like argumentation schemes and validating their applicability in real-world scenarios.
Anthology ID:
2025.acl-long.368
Volume:
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7421–7435
Language:
URL:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.368/
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Bibkey:
Cite (ACL):
Ramon Ruiz-Dolz, Zlata Kikteva, and John Lawrence. 2025. Mining Complex Patterns of Argumentative Reasoning in Natural Language Dialogue. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7421–7435, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
Mining Complex Patterns of Argumentative Reasoning in Natural Language Dialogue (Ruiz-Dolz et al., ACL 2025)
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https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.368.pdf