Arabic machine translation: a survey

Alqudsi, Arwa; Omar, Nazlia; Shaker, Khalid

doi:10.1007/s10462-012-9351-1

Cited by 56 publications

(38 citation statements)

References 35 publications

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“…Their evaluation is based on Universal Networking Language (UNL) and the Interlingua approach for machine translation. Reference [11] in Arabic Machine Translation Survey discussed the challenges for Arabic Machine Translation. The research found that it is difficult to find a suitable machine translation that meet human requirements.…”

Section: Review Of Relevant Literaturementioning

confidence: 99%

Assessing the Translation of Google and Microsoft Bing in Translating Political Texts from Arabic into English

Almahasees¹

2017

IJLLL

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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.

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Section: Review Of Relevant Literaturementioning

confidence: 99%

Assessing the Translation of Google and Microsoft Bing in Translating Political Texts from Arabic into English

Almahasees¹

2017

IJLLL

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“…For the Arabic and Indic families of languages, a character's glyph (that is, all the character's different possible representations) can form in four different shapes depending on the glyph's position in a word and the surrounding characters. Whether the letters are in initial, medial, end or isolated form, they will take on different shapes [16,17]. An example of the four different shapes of the Arabic character ‫"ض"‬ (Daad/ d) is illustrated in Table 1: The challenging part of contextual shaping is that, for all the different glyphs, there is only one defined code point in different encoding standards.…”

Section: Contextual Shapingmentioning

confidence: 99%

Localization to Bidirectional Languages for a Visual Programming Environment on Smartphones

2017

IJCSI

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Catrobat is a free and open source visual programming environment for smartphones. It has been developed for educational purposes to help students visualize and understand learning material. It allows students to build their own animations and games for their classes in academic subjects and wirelessly control external hardware. Catrobat needs to talk to the young children in schools in their native language and enable them to get the best experience in the language of their choice. In this paper, we localize Catrobat into bidirectional languages such as Arabic, Persian, Urdu, and Hebrew, and introduce the challenging aspects of localization to such languages. The localization testing results show that the product is cosmetically correct, linguistically accurate, and culturally appropriate. Therefore, it meets bidirectional requirements, complies with bidirectionality design guidelines, and can be employed in programming education for young schoolchildren.

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“…Farghaly (2010), for instance, took a few examples from the SYSTRAN Arabic to English machine translation system. Alqudsi et al (2012) too recommend to develop a transfer based MT system and a no-machine learning technique that fully meets human requirements. It is emphasized upon transfer-based technique to ensure that the meaning of the original sentence is captured before generating the correct translation.…”

Section: Prior Studiesmentioning

confidence: 99%

Artificial intelligence and natural language processing: the Arabic corpora in online translation software

Ali

2016

Int. j. adv. appl. sci

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Arabic machine translation: a survey

Cited by 56 publications

References 35 publications

Assessing the Translation of Google and Microsoft Bing in Translating Political Texts from Arabic into English

Assessing the Translation of Google and Microsoft Bing in Translating Political Texts from Arabic into English

Localization to Bidirectional Languages for a Visual Programming Environment on Smartphones

Artificial intelligence and natural language processing: the Arabic corpora in online translation software

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