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

CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction