The task of Grammatical Error Correction (GEC) aims to automatically correct grammatical errors in natural texts. Almost all previous works treat annotated training data equally, but inherent discrepancies in data are neglected. In this paper, the inherent discrepancies are manifested in two aspects, namely, accuracy of data annotation and diversity of potential annotations. To this end, we propose MainGEC, which designs token-level and sentence-level training weights based on inherent discrepancies in accuracy and potential diversity of data annotation, respectively, and then conducts mixed-grained weighted training to improve the training effect for GEC. Empirical evaluation shows that whether in the Seq2Seq or Seq2Edit manner, MainGEC achieves consistent and significant performance improvements on two benchmark datasets, demonstrating the effectiveness and superiority of the mixed-grained weighted training. Further ablation experiments verify the effectiveness of designed weights of both granularities in MainGEC.

本研究提出了MainGEC方法，通过利用数据标注的准确性和潜在注释的多样性的内在差异设计了基于令牌级和句子级训练权重，并进行了混合粒度加权训练以改善GEC的训练效果。经验证实，MainGEC在两个基准数据集上以Seq2Seq或Seq2Edit方式表现出一致且显著的性能提升，证明了混合粒度加权训练的有效性和优越性。进一步的消融实验验证了MainGEC中设计的两个粒度权重的有效性。

通过混合细粒度加权训练进行语法错误修正