DIA-English-Arabic neural machine translation domain: sulfur industry

Diadeen Ali Hameed, Tahseen Ameen Faisal, Alaa Khudhair Abbas, Harith Abdullah Ali, Ghanim Thiab Hasan


The aim of this paper is the design and development a new English-Arabic neural machine translation (NMT) called DIA translation system. The main purpose of the designing system is to study translator limited sulfur industry domain as a stand-alone tool in order to improve the translation quality. Machine translation (MT) are very sensitive to the domains they were trained on and can be integrated with general (English-Arabic) MT systems. The proposed system has mainly four directions: supports chemical symbols, terms, phrase, and text and it is evaluated by using (1,200) various English declarative sentences which written by English Language experts. The obtained results indicate that this system is high effective and has an accuracy of 79.33% in comparison with Google translator which has 38.67% for the same test samples.


DIA translator; English-Arabic translator; Google translator; Machine translation; Natural language processing; Neural approach

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DOI: http://doi.org/10.11591/ijeecs.v27.i3.pp1619-1624


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