arXiv：cs.LG· Zhijun Zhang, Qianlong Wang, Keyang Ding, Genan Dai, Bowen Zhang, Bin Liang, Ruifeng Xu, Yongsheng Liang·· 4 小时前AI 评分29
通过对抗强化学习改进论辩挖掘的合成数据生成
Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning
AI 导读
研究提出一种面向论辩挖掘的对抗强化学习框架，让生成器产出结构化论辩实例、判别器区分真实与合成数据，通过对抗反馈在保持多样性的同时持续提升结构准确度。在三个基准数据集上，该框架在全量与低资源设置下均稳定提升论辩挖掘性能，验证了其有效性与可扩展性。
正文
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.
| Comments: | Accepted to Findings of EMNLP 2026 |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.07699 [cs.LG] |
| (or arXiv:2610.07699v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07699 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhijun Zhang [view email]
[v1]
Tue, 6 Oct 2026 03:44:34 UTC (3,837 KB)
来源：arXiv：cs.LG · arxiv.org