@inproceedings{zhang-etal-2026-improving-synthetic,
title = "Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning",
author = "Zhang, Zhijun and
Wang, Qianlong and
Ding, Keyang and
Dai, Genan and
Zhang, Bowen and
Liang, Bin and
Xu, Ruifeng and
Liang, Yongsheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.950/",
pages = "20017--20036",
ISBN = "979-8-89176-462-0",
abstract = "Argument Mining (AM) is fundamentally constrained by the scarcity of high-quality structure-annotated datasets. While LLMs have shown promise in synthetic data generation, producing synthetic AM data that is both structurally accurate and sufficiently diverse remains a challenging problem. To address this problem, we revisit synthetic data generation for AM from a new perspective and propose a novel Adversarial Reinforcement Learning framework for Data Synthesis. The proposed framework jointly optimizes the generator and the discriminator in an adversarial loop, in which the generator produces structured AM instances, and the discriminator provides learning signals by distinguishing real data from synthetic candidates. This enables the generator to progressively improve both the structural accuracy of generated argument data while maintaining diversity through adversarial feedback. Extensive experiments demonstrate that the proposed framework consistently improves AM performance on three benchmark datasets in both full-data and low-resource settings, validating its effectiveness and scalability."
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<abstract>Argument Mining (AM) is fundamentally constrained by the scarcity of high-quality structure-annotated datasets. While LLMs have shown promise in synthetic data generation, producing synthetic AM data that is both structurally accurate and sufficiently diverse remains a challenging problem. To address this problem, we revisit synthetic data generation for AM from a new perspective and propose a novel Adversarial Reinforcement Learning framework for Data Synthesis. The proposed framework jointly optimizes the generator and the discriminator in an adversarial loop, in which the generator produces structured AM instances, and the discriminator provides learning signals by distinguishing real data from synthetic candidates. This enables the generator to progressively improve both the structural accuracy of generated argument data while maintaining diversity through adversarial feedback. Extensive experiments demonstrate that the proposed framework consistently improves AM performance on three benchmark datasets in both full-data and low-resource settings, validating its effectiveness and scalability.</abstract>
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%0 Conference Proceedings
%T Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning
%A Zhang, Zhijun
%A Wang, Qianlong
%A Ding, Keyang
%A Dai, Genan
%A Zhang, Bowen
%A Liang, Bin
%A Xu, Ruifeng
%A Liang, Yongsheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-improving-synthetic
%X Argument Mining (AM) is fundamentally constrained by the scarcity of high-quality structure-annotated datasets. While LLMs have shown promise in synthetic data generation, producing synthetic AM data that is both structurally accurate and sufficiently diverse remains a challenging problem. To address this problem, we revisit synthetic data generation for AM from a new perspective and propose a novel Adversarial Reinforcement Learning framework for Data Synthesis. The proposed framework jointly optimizes the generator and the discriminator in an adversarial loop, in which the generator produces structured AM instances, and the discriminator provides learning signals by distinguishing real data from synthetic candidates. This enables the generator to progressively improve both the structural accuracy of generated argument data while maintaining diversity through adversarial feedback. Extensive experiments demonstrate that the proposed framework consistently improves AM performance on three benchmark datasets in both full-data and low-resource settings, validating its effectiveness and scalability.
%U https://aclanthology.org/2026.findings-emnlp.950/
%P 20017-20036
Markdown (Informal)
[Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning](https://aclanthology.org/2026.findings-emnlp.950/) (Zhang et al., Findings 2026)
ACL
- Zhijun Zhang, Qianlong Wang, Keyang Ding, Genan Dai, Bowen Zhang, Bin Liang, Ruifeng Xu, and Yongsheng Liang. 2026. Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20017–20036, Budapest, Hungary. Association for Computational Linguistics.