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The present study compares the performance of three machine translation tools in English-Arabic translation to answer the questions of (a) whether the three machine translation tools, Google Translate, Systran and Microsoft Bing can be ordered in a hierarchy of performance, and (b) whether they can handle lexically and structurally ambiguous sentences and garden path sentences. Using a number of constructed and selected English sentences, the morphosyntactic features of number, gender, case, definiteness, and humanness, agreement between cardinal numerals and their head nouns, lexically and structurally ambiguous sentences and garden path sentences are used to test the three machine translation tools for performance. The results show that (a) as far as handling the morphosyntactic features of subject-verb agreement in Standard Arabic, all three machine translation tools perform equally well, and no machine translation tool seems to perform significantly better than the other two (b) some marked features (e.g. dual number and humanness) of SA seem to pose some problems for machine translation tools, and (c) lexically and structurally ambiguous sentences and garden path sentences seem to be the most challenging sentences for the three machine translation tools. [ABSTRACT FROM AUTHOR]

Simile is one of the most important literary devices. It is widely used as a figure of speech in literary works. However, simile can pose significant challenges in literary translation since different languages might use and interpret similes differently. The present research aims at investigating the translation strategies employed in Arabic to render English similes in a literary text. The translation model proposed by Pierini (2007) is utilized as a framework of this study. The researcher selected "The Old Man and The Sea" novel by Ernest Hemingway and its two Arabic translations as a case study. The novel's two Arabic translations are by The United Publishers referred to later as target text 1 (TT1) and Zyad Zakaria referred to later as target text 2 (TT2). First, the researcher randomly collected 40 similes as the study data. Then, their Arabic translations are identified. Next, the data is compared and analyzed to determine their translation techniques. After analysis, the research found that literal translation is a prominent strategy in rendering English similes to Arabic. [ABSTRACT FROM AUTHOR]