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Machine translation (MT) is an important sub-field of natural languageprocessing that aims to translate natural languages using computers. In recentyears, end-to-end neural machine translation (NMT) has achieved great successand has become the new mainstream method in practical MT systems. In thisarticle, we first provide a broad review of the methods for NMT and focus onmethods relating to architectures, decoding, and data augmentation. Then wesummarize the resources and tools that are useful for researchers. Finally, weconclude with a discussion of possible future research directions.

Neural Machine Translation: A Review of Methods, Resources, and Tools