MACHINE TRANSLATION METHODS AND SOME CURRENT TRENDS

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DOI:

https://doi.org/10.30888/2663-5712.2023-19-03-063

Keywords:

Rule-based machine translation (RBMT), Statistical machine translation (SMT), Neural machine translation (NMT), parallel corpora, encoder, decoder

Abstract

This paper provides an overview of some major machine translation methods designed to speed up the rate of multilingual text translation. Machine translation is achieved by computer software transforming text from one language to another. At present, d

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References

Bahdanau D,, et al. (2014). Neural Machine Translation by Jointly Learning to Align and Translate [Computer Science CoRR]. https://www.semanticscholar.org/paper/Neural-Machine-Translation-by-Jointly-Learning-to-Bahdanau-Cho/fa72afa9b2cbc8f0d7b05d52548906610ffbb9c5

Baniz B.(2020). Machine Translation: A Critical Look At The Performance Of Rule-Based And Statistical Machine Translation [Cadernos de Traducao], issue 40,vol 1 pp. 54-71. https://www.scielo.br/j/ct/a/6yh6JpvZDG4Rq6nFG6KQXmy/?lang=en

Koehn P., Och F.J., Marcu D. (2003). Statistical phrase-based machine translation [Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics], pp.127-133. https://aclanthology.org/N03-1017/

Shen G.R. (2010). Corpus-based Approach to Translation Studies [Cross Cultural Communication] issue 6, volume 4, pp.181-187. http://www.cscanada.net/index.php/ccc/article/view/j.ccc.1923670020100604.010

Published

2023-05-30

How to Cite

Шевченко, О. (2023). MACHINE TRANSLATION METHODS AND SOME CURRENT TRENDS. SWorldJournal, 3(19-03), 120–124. https://doi.org/10.30888/2663-5712.2023-19-03-063

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