@inproceedings{shi-cocarascu-2026-explainable,
title = "Explainable End-to-End Argument Mining with Toulmin-Structured Explanations",
author = "Shi, Zhiqiang and
Cocarascu, Oana",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.161/",
pages = "3575--3591",
ISBN = "979-8-89176-439-2",
abstract = "Large language models (LLMs) have advanced argument mining (AM), yet most approaches treat component classification, relation identification, and explanation generation as separate tasks, limiting coherence and explainability. We propose the first explainable end-to-end AM framework grounded in Toulmin{'}s theory, jointly predicting argument components, argumentative relations, and structured explanations in a single generation step. This unified formulation encourages LLMs to produce internally consistent argument graphs and reasoning traces aligned with their predictions. Further, we introduce five explainability criteria tailored to AM and grounded in Toulmin{'}s theory: completeness, correctness, faithfulness, soundness, usefulness, and develop a unified evaluation protocol combining structural metrics, neural similarity, LLM-based judgment, human evaluation and qualitative analysis. Extensive evaluation on several LLMs shows that our approach achieves competitive performance while substantially improving explainability, generating Toulmin-based explanations that are complete, grounded and faithful, uncovering reasoning errors not captured by standard AM metrics, and being consistently preferred in usefulness evaluations."
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<abstract>Large language models (LLMs) have advanced argument mining (AM), yet most approaches treat component classification, relation identification, and explanation generation as separate tasks, limiting coherence and explainability. We propose the first explainable end-to-end AM framework grounded in Toulmin’s theory, jointly predicting argument components, argumentative relations, and structured explanations in a single generation step. This unified formulation encourages LLMs to produce internally consistent argument graphs and reasoning traces aligned with their predictions. Further, we introduce five explainability criteria tailored to AM and grounded in Toulmin’s theory: completeness, correctness, faithfulness, soundness, usefulness, and develop a unified evaluation protocol combining structural metrics, neural similarity, LLM-based judgment, human evaluation and qualitative analysis. Extensive evaluation on several LLMs shows that our approach achieves competitive performance while substantially improving explainability, generating Toulmin-based explanations that are complete, grounded and faithful, uncovering reasoning errors not captured by standard AM metrics, and being consistently preferred in usefulness evaluations.</abstract>
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%0 Conference Proceedings
%T Explainable End-to-End Argument Mining with Toulmin-Structured Explanations
%A Shi, Zhiqiang
%A Cocarascu, Oana
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F shi-cocarascu-2026-explainable
%X Large language models (LLMs) have advanced argument mining (AM), yet most approaches treat component classification, relation identification, and explanation generation as separate tasks, limiting coherence and explainability. We propose the first explainable end-to-end AM framework grounded in Toulmin’s theory, jointly predicting argument components, argumentative relations, and structured explanations in a single generation step. This unified formulation encourages LLMs to produce internally consistent argument graphs and reasoning traces aligned with their predictions. Further, we introduce five explainability criteria tailored to AM and grounded in Toulmin’s theory: completeness, correctness, faithfulness, soundness, usefulness, and develop a unified evaluation protocol combining structural metrics, neural similarity, LLM-based judgment, human evaluation and qualitative analysis. Extensive evaluation on several LLMs shows that our approach achieves competitive performance while substantially improving explainability, generating Toulmin-based explanations that are complete, grounded and faithful, uncovering reasoning errors not captured by standard AM metrics, and being consistently preferred in usefulness evaluations.
%U https://aclanthology.org/2026.aacl-long.161/
%P 3575-3591
Markdown (Informal)
[Explainable End-to-End Argument Mining with Toulmin-Structured Explanations](https://aclanthology.org/2026.aacl-long.161/) (Shi & Cocarascu, AACL-IJCNLP 2026)
ACL
- Zhiqiang Shi and Oana Cocarascu. 2026. Explainable End-to-End Argument Mining with Toulmin-Structured Explanations. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3575–3591, Hengqin, China. Association for Computational Linguistics.