  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.
