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- ISSN: 2382-6282 (Print); 2972-3108 (Online)
- Abbreviated Title: Int. J. Lang. Lit. Linguist.
- Frequency: Bimonthly
- DOI: 10.18178/IJLLL
- Editor-in-Chief: Dr. Jason Miin-Hwa Lim
- Managing Editor: Jennifer X. Zeng
- Indexed by: CNKI, Google Scholar, Crossref,
- E-mail: ijlll_Editor@126.com
IJLLL 2017 Vol.3(1): 1-4 ISSN: 2382-6282
DOI: 10.18178/IJLLL.2017.3.1.100
DOI: 10.18178/IJLLL.2017.3.1.100
Assessing the Translation of Google and Microsoft Bing in Translating Political Texts from Arabic into English
Zakaryia Mustafa Almahasees
Abstract—Online machine translation (OMT) systems are widely used throughout the world freely or at low cost. Most of these systems use statistical machine translation (SMT) that is based on a corpus full with translation examples to learn from them how to translate correctly. Online automatic machine translation systems differ widely in their effectiveness and accuracy. Therefore, the wide spread of such translation platforms make it necessary to evaluate the output in order to shed light on the capacity and usability of each system. The present study have selected the most prominent translation systems, Google and Microsoft to test which system is better and more reliable in rendering English<>Arabic translation. To conduct the study, the researcher has chosen automatic evaluation of the two system outputs by using the most popular automatic evacuation metric BLEU. The study’s corpus consists of 25 Arabic sentences extracted from Petra News Agency of Jordan with its human reference translation from the English version of Petra. The result of the research showed that Google translate achieves better results than Microsoft Bing in comparison to human referenced translation. However, Machine Translation (MT) is still far from reaching fully automatic translation of a quality obtained by human translators.
Index Terms—Automatic evaluation, Arabic machine translation (AMT), Arabic MT evaluation, Google evaluation, Microsoft Bing evaluation, BLEU.
Zakaryia Almahasees is with University of Western Australia, Australia (e-mail: zakaryia.almahasees@research.uwa.edu.au).
Index Terms—Automatic evaluation, Arabic machine translation (AMT), Arabic MT evaluation, Google evaluation, Microsoft Bing evaluation, BLEU.
Zakaryia Almahasees is with University of Western Australia, Australia (e-mail: zakaryia.almahasees@research.uwa.edu.au).
Cite:Zakaryia Mustafa Almahasees, "Assessing the Translation of Google and Microsoft Bing in Translating Political Texts from Arabic into English," International Journal of Languages, Literature and Linguistics vol. 3, no. 1, pp. 1-4, 2017.
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