  We combine two of the most popular approaches to automated Grammatical Error
Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC
based on Neural Machine Translation (NMT). The hybrid system achieves new
state-of-the-art results on the CoNLL-2014 and JFLEG benchmarks. This GEC
system preserves the accuracy of SMT output and, at the same time, generates
more fluent sentences as it typical for NMT. Our analysis shows that the
created systems are closer to reaching human-level performance than any other
GEC system reported so far.
