  We describe two entries from the Cambridge University Engineering Department
to the BEA 2019 Shared Task on grammatical error correction. Our submission to
the low-resource track is based on prior work on using finite state transducers
together with strong neural language models. Our system for the restricted
track is a purely neural system consisting of neural language models and neural
machine translation models trained with back-translation and a combination of
checkpoint averaging and fine-tuning -- without the help of any additional
tools like spell checkers. The latter system has been used inside a separate
system combination entry in cooperation with the Cambridge University Computer
Lab.
