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。