Preprint Open access
Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning
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 r …