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An In-depth Walkthrough on Evolution of Neural Machine Translation

Published 10 Apr 2020 in cs.CL, cs.LG, and cs.NE | (2004.04902v1)

Abstract: Neural Machine Translation (NMT) methodologies have burgeoned from using simple feed-forward architectures to the state of the art; viz. BERT model. The use cases of NMT models have been broadened from just language translations to conversational agents (chatbots), abstractive text summarization, image captioning, etc. which have proved to be a gem in their respective applications. This paper aims to study the major trends in Neural Machine Translation, the state of the art models in the domain and a high level comparison between them.

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