  The field of machine translation (MT), the automatic translation of written
text from one natural language into another, has experienced a major paradigm
shift in recent years. Statistical MT, which mainly relies on various
count-based models and which used to dominate MT research for decades, has
largely been superseded by neural machine translation (NMT), which tackles
translation with a single neural network. In this work we will trace back the
origins of modern NMT architectures to word and sentence embeddings and earlier
examples of the encoder-decoder network family. We will conclude with a survey
of recent trends in the field.
