Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning

Excerpt

arXiv:2610.07699v1 Announce Type: new 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