Abstract
Neural machine translation (NMT), powered by deep learning, is an emerging machine translation paradigm that has been advancing rapidly in recent years. It has become mainstream technology in both academia and industry of machine translation. This paper provides an overview of our research work on NMT. It particularly focuses on a series of NMT models proposed for considering a variety of useful information and knowledge constraints, which include variational NMT with constraints of latent variables, NMT advised by statistical machine translation, and NMT with syntactical constraints from the source language. In addition to this overview, this paper presents an outlook of the future trends in NMT.
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