Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning

The paper introduces an adversarial reinforcement learning framework to generate synthetic data for Argument Mining (AM). By jointly training a generator and a discriminator, the system produces structured AM instances that are both accurate and diverse. Experiments show consistent performance gains on three benchmark datasets in both full-data and low-resource scenarios.

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• llms
• reinforcement-learning
• benchmarks
• safety