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The incorporation of pseudo data in the training of grammatical errorcorrection models has been one of the main factors in improving the performanceof such models. However, consensus is lacking on experimental configurations,namely, choosing how the pseudo data should be generated or used. In thisstudy, these choices are investigated through extensive experiments, andstate-of-the-art performance is achieved on the CoNLL-2014 test set($F_{0.5}=65.0$) and the official test set of the BEA-2019 shared task($F_{0.5}=70.2$) without making any modifications to the model architecture.

An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction