Synthetic data generation is widely known to boost the accuracy of neural
grammatical error correction (GEC) systems, but existing methods often lack
diversity or are too simplistic to generate the broad range of grammatical
errors made by human writers. In this work, we use error type tags from
automatic annotation tools such as ERRANT to guide synthetic data generation.
We compare several models that can produce an ungrammatical sentence given a
clean sentence and an error type tag. We use these models to build a new, large
synthetic pre-training data set with error tag frequency distributions matching
a given development set. Our synthetic data set yields large and consistent
gains, improving the state-of-the-art on the BEA-19 and CoNLL-14 test sets. We
also show that our approach is particularly effective in adapting a GEC system,
trained on mixed native and non-native English, to a native English test set,
even surpassing real training data consisting of high-quality sentence pairs.

本研究使用自动注释工具 ERRANT 的错误类型标签指导合成数据生成，并比较了多种模型以研究如何基于错误类型标签从正确的句子生成不合语法的句子。在此基础上，构建一个规模较大的合成数据集，其错误标签的频率分布与给定开发集相匹配。该合成数据集具有较好的性能表现，可以显著提高 BEA-19 和 CoNLL14 测试集的性能，并且可以有效地适应 GEC 系统，特别是对于混合本地和非本地语言的英语人群，其效果优于由高质量句对组成的真实训练数据。