  Character-based neural machine translation (NMT) models alleviate
out-of-vocabulary issues, learn morphology, and move us closer to completely
end-to-end translation systems. Unfortunately, they are also very brittle and
easily falter when presented with noisy data. In this paper, we confront NMT
models with synthetic and natural sources of noise. We find that
state-of-the-art models fail to translate even moderately noisy texts that
humans have no trouble comprehending. We explore two approaches to increase
model robustness: structure-invariant word representations and robust training
on noisy texts. We find that a model based on a character convolutional neural
network is able to simultaneously learn representations robust to multiple
kinds of noise.
