Data Augmentation through generating pseudo data has been proven effective in
mitigating the challenge of data scarcity in the field of Grammatical Error
Correction (GEC). Various augmentation strategies have been widely explored,
most of which are motivated by two heuristics, i.e., increasing the
distribution similarity and diversity of pseudo data. However, the underlying
mechanism responsible for the effectiveness of these strategies remains poorly
understood. In this paper, we aim to clarify how data augmentation improves GEC
models. To this end, we introduce two interpretable and computationally
efficient measures: Affinity and Diversity. Our findings indicate that an
excellent GEC data augmentation strategy characterized by high Affinity and
appropriate Diversity can better improve the performance of GEC models. Based
on this observation, we propose MixEdit, a data augmentation approach that
strategically and dynamically augments realistic data, without requiring extra
monolingual corpora. To verify the correctness of our findings and the
effectiveness of the proposed MixEdit, we conduct experiments on mainstream
English and Chinese GEC datasets. The results show that MixEdit substantially
improves GEC models and is complementary to traditional data augmentation
methods.

通过生成伪数据进行数据增强已被证明在语法错误修正（GEC）领域中缓解数据稀缺性的挑战中有效。本文引入了可解释且计算高效的两个度量：亲和性和多样性，研究发现高亲和性且适当多样性的优秀 GEC 数据增强策略可以更好地提高 GEC 模型的性能，并提出了一种名为 MixEdit 的数据增强方法，通过策略性和动态地增加真实数据而不需要额外的单语语料库。在英文和中文 GEC 数据集上的实验证实了我们发现的正确性和 MixEdit 的有效性，结果表明 MixEdit 显著提高 GEC 模型性能并且与传统数据增强方法互补。