Abstract
In recent years, an exponential development in the field of Natural Language Processing (NLP) has been witnessed with the integration of deep learning approaches. The incorporation of neural networks in NLP showed promising results and opened avenues for more research which in turn accelerated the advancement of NLP technologies. Machine Translation which is the subfield of NLP became better with neural networks and widely gained acceptance in industry and academia as well. The paper aims to capture various deep learning and attention techniques that are currently being in used to automatize translation task and enhance the translation quality. Later in the paper conclusion and future prospects are discussed.
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