  Neural Machine Translation (NMT) systems are known to degrade when confronted
with noisy data, especially when the system is trained only on clean data. In
this paper, we show that augmenting training data with sentences containing
artificially-introduced grammatical errors can make the system more robust to
such errors. In combination with an automatic grammar error correction system,
we can recover 1.5 BLEU out of 2.4 BLEU lost due to grammatical errors. We also
present a set of Spanish translations of the JFLEG grammar error correction
corpus, which allows for testing NMT robustness to real grammatical errors.
