Authors: Basem Alkazemi; Mohammed Nour; Atif Naseer; Ammar Natto; Grami Grami
Addresses: Department of Computer Science, Umm Al-Qura University, Saudi Arabia ' Department of Computer Science, Umm Al-Qura University, Saudi Arabia ' Science and Technology Unit, Umm Al-Qura University, Saudi Arabia ' Deanship of Scientific Research, Umm Al-Qura University, Saudi Arabia ' Applied Linguistics, King Abdulaziz University, Saudi Arabia
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.
International Journal of Innovation and Learning, 2019 Vol.26 No.2, pp.115 - 130
Available online: 24 Jun 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article