  Machine translation (MT) is an important sub-field of natural language
processing that aims to translate natural languages using computers. In recent
years, end-to-end neural machine translation (NMT) has achieved great success
and has become the new mainstream method in practical MT systems. In this
article, we first provide a broad review of the methods for NMT and focus on
methods relating to architectures, decoding, and data augmentation. Then we
summarize the resources and tools that are useful for researchers. Finally, we
conclude with a discussion of possible future research directions.
