The Open Argument Mining Framework

Debela Gemechu, Ramon Ruiz-Dolz, Kamila Górska, Somaye Moslemnejad, Eimear Maguire, Dimitra Zografistou, Yohan Jo, John Lawrence, Chris Reed


Abstract
Despite extensive research in Argument Mining (AM), the field faces significant challenges in limited reproducibility, difficulty in comparing systems due to varying task combinations, and a lack of interoperability caused by the heterogeneous nature of argumentation theory. These challenges are further exacerbated by the absence of dedicated tools, with most advancements remaining isolated research outputs rather than reusable systems. The oAMF (Open Argument Mining Framework) addresses these issues by providing an open-source, modular, and scalable platform that unifies diverse AM methods. Initially released with seventeen integrated modules, the oAMF serves as a starting point for researchers and developers to build, experiment with, and deploy AM pipelines while ensuring interoperability and allowing multiple theories of argumentation to co-exist within the same framework. Its flexible design supports integration via Python APIs, drag-and-drop tools, and web interfaces, streamlining AM development for research and industry setup, facilitating method comparison, and reproducibility.
Anthology ID:
2025.acl-demo.31
Volume:
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Pushkar Mishra, Smaranda Muresan, Tao Yu
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
318–328
Language:
URL:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.31/
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Bibkey:
Cite (ACL):
Debela Gemechu, Ramon Ruiz-Dolz, Kamila Górska, Somaye Moslemnejad, Eimear Maguire, Dimitra Zografistou, Yohan Jo, John Lawrence, and Chris Reed. 2025. The Open Argument Mining Framework. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 318–328, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
The Open Argument Mining Framework (Gemechu et al., ACL 2025)
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https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.31.pdf
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 2025.acl-demo.31.copyright_agreement.pdf