A SYSTEMATIC READING IN STATISTICAL TRANSLATION: FROM THE STATISTICAL MACHINE TRANSLATION TO THE NEURAL TRANSLATION MODELS

Achieving high accuracy in automatic translation tasks has been one of the challenging goals for researchers in the area of machine translation since decades. Thus, the eagerness of exploring new possible ways to improve machine translation was always the matter for researchers in the field. Automat...

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Veröffentlicht in:Journal of ICT Jg. 16; H. 2; S. 408
Hauptverfasser: El Maazouzi, Zakaria, El Mohajir, Badr Eddine, Al Achhab, Mohammed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kedah Darul Aman Universiti Utara Malaysia 01.12.2017
UUM Press
Schlagworte:
Machine translation
Natural language processing
neural language model
Neural networks
recurrent neural networks
ISSN:1675-414X, 2180-3862
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Zusammenfassung:Achieving high accuracy in automatic translation tasks has been one of the challenging goals for researchers in the area of machine translation since decades. Thus, the eagerness of exploring new possible ways to improve machine translation was always the matter for researchers in the field. Automatic translation as a key application in the natural language processing domain has developed many approaches, namely statistical machine translation and recently neural machine translation that improved largely the translation quality especially for Latin languages. They have even made it possible for the translation of some language pairs to approach human translation quality. In this paper, we present a survey of the state of the art of statistical translation, where we describe the different existing methodologies, and we overview the recent research studies while pointing out the main strengths and limitations of the different approaches.
Bibliographie:ObjectType-Article-1
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ObjectType-Feature-2
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ISSN:1675-414X
2180-3862
DOI:10.32890/jict2017.16.2.11