  Grammatical error correction can be viewed as a low-resource
sequence-to-sequence task, because publicly available parallel corpora are
limited. To tackle this challenge, we first generate erroneous versions of
large unannotated corpora using a realistic noising function. The resulting
parallel corpora are subsequently used to pre-train Transformer models. Then,
by sequentially applying transfer learning, we adapt these models to the domain
and style of the test set. Combined with a context-aware neural spellchecker,
our system achieves competitive results in both restricted and low resource
tracks in ACL 2019 BEA Shared Task. We release all of our code and materials
for reproducibility.
