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

被引:0
|
作者
Alkazemi, Basem [1 ]
Nour, Mohammed [1 ]
Naseer, Atif [2 ]
Natto, Ammar [3 ]
Grami, Grami [4 ]
机构
[1] Umm Al Qura Univ, Dept Comp Sci, Mecca, Saudi Arabia
[2] Umm Al Qura Univ, Sci & Technol Unit, Mecca, Saudi Arabia
[3] Umm Al Qura Univ, Deanship Sci Res, Mecca, Saudi Arabia
[4] King Abdulaziz Univ, Appl Linguist, Riyadh, Saudi Arabia
关键词
machine translation; rule-based translation; statistical-based translation; Stanford CoreNLP; Azure Translation Hub; Google neural machine translation; NLP; Arabic machine translation;
D O I
暂无
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
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 cliches, 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.
引用
收藏
页码:115 / 130
页数:16
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