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.

本文研究了基于字符的神经机器翻译模型，并发现它们能够解决词表外的问题、学习词形变化，但是在面对嘈杂的数据时容易出现错误。作者探究了两个方法来提高模型的鲁棒性：结构不变的词表示和在噪声数据上强化训练。作者发现一个基于字符卷积神经网络的模型能够同时学习多种噪声下的鲁棒表示。