  Previously, neural methods in grammatical error correction (GEC) did not
reach state-of-the-art results compared to phrase-based statistical machine
translation (SMT) baselines. We demonstrate parallels between neural GEC and
low-resource neural MT and successfully adapt several methods from low-resource
MT to neural GEC. We further establish guidelines for trustable results in
neural GEC and propose a set of model-independent methods for neural GEC that
can be easily applied in most GEC settings. Proposed methods include adding
source-side noise, domain-adaptation techniques, a GEC-specific
training-objective, transfer learning with monolingual data, and ensembling of
independently trained GEC models and language models. The combined effects of
these methods result in better than state-of-the-art neural GEC models that
outperform previously best neural GEC systems by more than 10% M$^2$ on the
CoNLL-2014 benchmark and 5.9% on the JFLEG test set. Non-neural
state-of-the-art systems are outperformed by more than 2% on the CoNLL-2014
benchmark and by 4% on JFLEG.
