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.
CharNMT is brittle This paper investigates the impact of character-level noise on 4 different neural machine translation systems This paper empirically investigates the performance of character-level NMT systems in the face of character-level noise, both synthesized and natural. This paper investigates the impact of noisy input on Machine Translation and tests ways to make NMT models more robust