An In-depth Walkthrough on Evolution of Neural Machine Translation
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arXiv
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| Format: | Preprint |
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2020
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| _version_ | 1866912011183456256 |
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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 |
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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 |