@inproceedings{sun-etal-2025-learning,
title = "Learning First-Order Logic Rules for Argumentation Mining",
author = "Sun, Yang and
Chen, Guanrong and
Alinejad-Rokny, Hamid and
Bao, Jianzhu and
Huang, Yuqi and
Liang, Bin and
Wong, Kam-Fai and
Yang, Min and
Xu, Ruifeng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.691/",
doi = "10.18653/v1/2025.acl-long.691",
pages = "14133--14148",
ISBN = "979-8-89176-251-0",
abstract = "Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs). While previous works focus on representation learning to encode ACs and AC pairs, they fail to explicitly model the underlying reasoning patterns of AM, resulting in limited interpretability. This paper proposes a novel $\underline{F}$irst-$\underline{O}$rder $\underline{L}$ogic reasoning framework for $\underline{AM}$ (FOL-AM), designed to explicitly capture logical reasoning paths within argumentative texts. By interpreting multiple AM subtasks as a unified relation query task modeled using FOL rules, FOL-AM facilitates multi-hop relational reasoning and enhances interpretability. The framework supports two flexible implementations: a fine-tuned approach to leverage task-specific learning, and a prompt-based method utilizing large language models to harness their generalization capabilities. Extensive experiments on two AM benchmarks demonstrate that FOL-AM outperforms strong baselines while significantly improving explainability."
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<abstract>Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs). While previous works focus on representation learning to encode ACs and AC pairs, they fail to explicitly model the underlying reasoning patterns of AM, resulting in limited interpretability. This paper proposes a novel \underlineFirst-\underlineOrder \underlineLogic reasoning framework for \underlineAM (FOL-AM), designed to explicitly capture logical reasoning paths within argumentative texts. By interpreting multiple AM subtasks as a unified relation query task modeled using FOL rules, FOL-AM facilitates multi-hop relational reasoning and enhances interpretability. The framework supports two flexible implementations: a fine-tuned approach to leverage task-specific learning, and a prompt-based method utilizing large language models to harness their generalization capabilities. Extensive experiments on two AM benchmarks demonstrate that FOL-AM outperforms strong baselines while significantly improving explainability.</abstract>
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%0 Conference Proceedings
%T Learning First-Order Logic Rules for Argumentation Mining
%A Sun, Yang
%A Chen, Guanrong
%A Alinejad-Rokny, Hamid
%A Bao, Jianzhu
%A Huang, Yuqi
%A Liang, Bin
%A Wong, Kam-Fai
%A Yang, Min
%A Xu, Ruifeng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F sun-etal-2025-learning
%X Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs). While previous works focus on representation learning to encode ACs and AC pairs, they fail to explicitly model the underlying reasoning patterns of AM, resulting in limited interpretability. This paper proposes a novel \underlineFirst-\underlineOrder \underlineLogic reasoning framework for \underlineAM (FOL-AM), designed to explicitly capture logical reasoning paths within argumentative texts. By interpreting multiple AM subtasks as a unified relation query task modeled using FOL rules, FOL-AM facilitates multi-hop relational reasoning and enhances interpretability. The framework supports two flexible implementations: a fine-tuned approach to leverage task-specific learning, and a prompt-based method utilizing large language models to harness their generalization capabilities. Extensive experiments on two AM benchmarks demonstrate that FOL-AM outperforms strong baselines while significantly improving explainability.
%R 10.18653/v1/2025.acl-long.691
%U https://aclanthology.org/2025.acl-long.691/
%U https://doi.org/10.18653/v1/2025.acl-long.691
%P 14133-14148
Markdown (Informal)
[Learning First-Order Logic Rules for Argumentation Mining](https://aclanthology.org/2025.acl-long.691/) (Sun et al., ACL 2025)
ACL
- Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, and Ruifeng Xu. 2025. Learning First-Order Logic Rules for Argumentation Mining. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14133–14148, Vienna, Austria. Association for Computational Linguistics.