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This research addresses the critical bottleneck of scarce annotated data in argument mining by introducing an adversarial reinforcement learning framework to generate high-quality synthetic datasets. By jointly optimizing a generator and discriminator, the method successfully enhances both structural accuracy and diversity, outperforming standard approaches on multiple benchmarks even in low-resource scenarios. The work demonstrates that advanced generative techniques can effectively scale NLP tasks without relying solely on massive human-labeled corpora.
Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning
arXiv:2610.07699v1 Announce Type: cross 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: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.
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2610.07699 [cs.LG]
(or arXiv:2610.07699v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.07699 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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