Neural machine translation aims at building a single large neural network
that can be trained to maximize translation performance. The encoder-decoder
architecture with an attention mechanism achieves a translation performance
comparable to the existing state-of-the-art phrase-based systems on the task of
English-to-French translation. However, the use of large vocabulary becomes the
bottleneck in both training and improving the performance. In this paper, we
propose an efficient architecture to train a deep character-level neural
machine translation by introducing a decimator and an interpolator. The
decimator is used to sample the source sequence before encoding while the
interpolator is used to resample after decoding. Such a deep model has two
major advantages. It avoids the large vocabulary issue radically; at the same
time, it is much faster and more memory-efficient in training than conventional
character-based models. More interestingly, our model is able to translate the
misspelled word like human beings.

本文提出了一种基于深度字符级神经机器翻译的高效架构，可以大大提高训练速度和内存效率，同时解决了大词汇量的问题，并实现了像人一样翻译错别字的能力。