Exploring Quality and Diversity in Synthetic Data Generation for Argument Mining

The advancement of Argument Mining (AM) is hindered by a critical bottleneck: the scarcity of structure-annotated datasets, which are expensive to create manually. Inspired by recent successes in synthetic data generation across various NLP tasks, this paper explores methodologies for LLMs to generate synthetic data for AM. We investigate two complementary synthesis perspectives: a quality-oriented synthesis approach, which employs structure-aware paraphrasing to preserve annotation quality, and a diversity-oriented synthesis approach, which generates novel argumentative texts with diverse topics and argument structures. Experiments on three datasets show that augmenting original training data with our synthetic data, particularly when combining both quality-and diversity-oriented instances, significantly enhances the performance of existing AM models, both in full-data and low-resource settings. Moreover, the positive correlation between synthetic data volume and model performance highlights the scalability of our methods.