An In-depth Walkthrough on Evolution of Neural Machine Translation

Fuente: arXiv
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Main Authors: Jagtap, Rohan, Dhage, Sudhir N.
Format: Preprint
Published: 2020
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author Jagtap, Rohan
Dhage, Sudhir N.
author_facet Jagtap, Rohan
Dhage, Sudhir N.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2004_04902
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle An In-depth Walkthrough on Evolution of Neural Machine Translation
Jagtap, Rohan
Dhage, Sudhir N.
Computation and Language
Machine Learning
Neural and Evolutionary Computing
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.
title An In-depth Walkthrough on Evolution of Neural Machine Translation
topic Computation and Language
Machine Learning
Neural and Evolutionary Computing
url https://arxiv.org/abs/2004.04902