文章基本信息

标题：Evaluating Urdu to Arabic Machine Translation Tools
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作者：Maheen Akhter Ayesha ; Sahar Noor ; Muhammad Ramzan 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2017
卷号：8
期号：10
DOI：10.14569/IJACSA.2017.081012
出版社：Science and Information Society (SAI)
摘要：Machine translation is an active research domain in fields of artificial intelligence. The relevant literature presents a number of machine translation approaches for the translation of different languages. Urdu is the national language of Pakistan while Arabic is a major language in almost 20 different countries of the world comprising almost 450 million people. To the best of our knowledge, there is no published research work presenting any method on machine translation from Urdu to Arabic, however, some online machine translation systems like Google , Bing and Babylon provide Urdu to Arabic machine translation facility. In this paper, we compare the performance of online machine translation systems. The input in Urdu language is translated by the systems and the output in Arabic is compared with the ground truth data of Arabic reference sentences. The comparative analysis evaluates the systems by three performance evaluation measures: BLEU (BiLingual Evaluation Understudy), METEOR (Metric for Evaluation of Translation with Explicit ORdering) and NIST (National Institute of Standard and Technology) with the help of a standard corpus. The results show that Google translator is far better than Bing and Babylon translators. It outperforms, on the average, Babylon by 28.55% and Bing by 15.74%.
关键词：Natural language processing; machine translation; Urdu-Arabic Corpus; Google; Bing; Babylon; translator; BiLingual Evaluation Understudy (BLEU); National Institute of Standard and Technology (NIST); Metric for Evaluation of Translation with Explicit ORder (METEOR)