  We perform neural machine translation of sentence fragments in order to
create large amounts of training data for English grammatical error correction.
Our method aims at simulating mistakes made by second language learners, and
produces a wider range of non-native style language in comparison to
state-of-the-art synthetic data creation methods. In addition to purely
grammatical errors, our approach generates other types of errors, such as
lexical errors. We perform grammatical error correction experiments using
neural sequence-to-sequence models, and carry out quantitative and qualitative
evaluation. A model trained on data created using our proposed method is shown
to outperform a baseline model on test data with a high proportion of errors.
