  Machine translation (MT) is an area of study in Natural Language processing
which deals with the automatic translation of human language, from one language
to another by the computer. Having a rich research history spanning nearly
three decades, Machine translation is one of the most sought after area of
research in the linguistics and computational community. In this paper, we
investigate the models based on deep learning that have achieved substantial
progress in recent years and becoming the prominent method in MT. We shall
discuss the two main deep-learning based Machine Translation methods, one at
component or domain level which leverages deep learning models to enhance the
efficacy of Statistical Machine Translation (SMT) and end-to-end deep learning
models in MT which uses neural networks to find correspondence between the
source and target languages using the encoder-decoder architecture. We conclude
this paper by providing a time line of the major research problems solved by
the researchers and also provide a comprehensive overview of present areas of
research in Neural Machine Translation.
