  In this paper, we describe our systems submitted to the Building Educational
Applications (BEA) 2019 Shared Task (Bryant et al., 2019). We participated in
all three tracks. Our models are NMT systems based on the Transformer model,
which we improve by incorporating several enhancements: applying dropout to
whole source and target words, weighting target subwords, averaging model
checkpoints, and using the trained model iteratively for correcting the
intermediate translations. The system in the Restricted Track is trained on the
provided corpora with oversampled "cleaner" sentences and reaches 59.39 F0.5
score on the test set. The system in the Low-Resource Track is trained from
Wikipedia revision histories and reaches 44.13 F0.5 score. Finally, we finetune
the system from the Low-Resource Track on restricted data and achieve 64.55
F0.5 score, placing third in the Unrestricted Track.
