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We introduce unsupervised techniques based on phrase-based statisticalmachine translation for grammatical error correction (GEC) trained on a pseudolearner corpus created by Google Translation. We verified our GEC systemthrough experiments on various GEC dataset, includi ng a low resource track ofthe shared task at Building Educational Applications 2019 (BEA 2019). As aresult, we achieved an F_0.5 score of 28.31 points with the test data of thelow resource track.

Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus