Abstract
Machine translation systems face many challenges when dealing with Arabic lexical ambiguity, which affects the quality and accuracy of their translations. This study investigates how two popular MT systems, Google Translate and SYSTRAN, handle three problematic linguistic features of the Arabic language: homonyms, heteronyms, and polysemes. A test suite was designed to include sentences that contain these features in different contexts and domains. The translations produced by the two MT systems were evaluated by four independent evaluators for intelligibility and accuracy using a four-point scale. Results showed that both MT systems struggled with the three chosen linguistic features, with average scores below 40%. Google Translate outperformed SYSTRAN in almost every sentence in the test suite. Both systems scored better in intelligibility than in accuracy. Heteronyms proved to be the most challenging for both MT systems due to the unique design of Arabic discretization, which is not yet recognized by translation systems. This study contributes to the field of machine translation by providing a comprehensive analysis of Arabic lexical ambiguity and its impact on MT quality, as well as suggesting possible ways to improve MT systems for Arabic-English translation.
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