English(EN) Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning

新的对抗性强化学习框架增强了用于论点挖掘的合成数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了一种新颖的对抗性强化学习框架，以改进用于论点挖掘的合成数据生成。该方法使用生成器创建结构化的论点挖掘实例，并使用判别器区分真实数据和合成数据，从而提供反馈以提高生成器的准确性和多样性。实验表明，该方法在基准数据集上显著提高了论点挖掘的性能，即使在低资源场景下也证明是有效的。 AI

影响提高了论点挖掘任务的训练数据的质量和数量，有可能加速自然语言处理领域的研究和应用开发。

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arXiv cs.CL TIER_1 English(EN) · Zhijun Zhang, Qianlong Wang, Keyang Ding, Genan Dai, Bowen Zhang, Bin Liang, Ruifeng Xu, Yongsheng Liang · 2026-10-07 04:00

通过对抗性强化学习改进用于论点挖掘的合成数据生成

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 accu…