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