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Old Age
An In-depth Walkthrough on Evolution of Neural Machine Translation
April 10, 2020 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Rohan Jagtap, Sudhir N. Dhage
arXiv ID
2004.04902
Category
cs.CL: Computation & Language
Cross-listed
cs.LG,
cs.NE
Citations
0
Venue
arXiv.org
Last Checked
6 months ago
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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