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Synthetic data generation is widely known to boost the accuracy of neuralgrammatical error correction (GEC) systems, but existing methods often lackdiversity or are too simplistic to generate the broad range of grammaticalerrors made by human writers. In this work, we use error type tags fromautomatic annotation tools such as ERRANT to guide synthetic data generation.We compare several models that can produce an ungrammatical sentence given aclean sentence and an error type tag. We use these models to build a new, largesynthetic pre-training data set with error tag frequency distributions matchinga given development set. Our synthetic data set yields large and consistentgains, improving the state-of-the-art on the BEA-19 and CoNLL-14 test sets. Wealso 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.

Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models