AccurIT: a prototype of a machine translation engine for English to Arabic translation

Basem Alkazemi, Mohammed Nour, Atif Naseer, Ammar Natto and Grami Grami

International Journal of Innovation and Learning, 2019, vol. 26, issue 2, 115-130

Abstract: Current machine translators have reached an unprecedented level of sophistication in dealing with not only isolated words, but also longer sentences and paragraphs. Despite the advances achieved in this field, several challenges remain to be resolved for machine translation (MT) to be on par with professional human translation, including the quality of grammar and context accuracy, pragmatics, relevance, choice of vocabulary and ability to translate large files effectively based on this list's criteria. Another extremely problematic area that we have observed is incorrect literal translation of English phrases, proverbs, idioms, figurative speech and clichés, which proves to be an issue with most current translation programs, even ones built using a phrase-based approach. Therefore, this study's objective was to develop a prototype of an English-Arabic MT engine, AccurIT, to address MTs' English-to-Arabic translation-accuracy issues in general. We compared the results of our tool against Google and Azure Translation Hub based on some excerpts from the legal realm to demonstrate AccurIT's efficacy, and the results are promising.

Keywords: machine translation; rule-based translation; statistical-based translation; Stanford CoreNLP; Azure Translation Hub; Google neural machine translation; NLP; Arabic machine translation. (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=101274 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijilea:v:26:y:2019:i:2:p:115-130

Access Statistics for this article

More articles in International Journal of Innovation and Learning from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().