Machine Translation Errors

Machine translation (MT) systems are widely used throughout the world freely or at low cost. The spread of MT entails a thorough analysis of translation produced by such translation systems. The present study evaluates the capacity of two MT systems-Google Translate and Microsoft Bing translator-in translation from Arabic into English of Khalil Gibran's literary masterpiece-The Prophet (2000). The question that arises in the study is could we trust MT in the translation of literary masterpieces across languages and particularly from Arabic to English? How close does MT output to human translation? To conduct that, the study is adopted Bilingual Evaluation Understudy (BLEU) of Papineni (2000). MT output analysis showed that MT is not accurate, intelligible and natural in translating literary texts due to the difficulty of literary texts, as they are full of metaphors and cultural specifications. Besides, there are some linguistic errors: lexical, syntactic and misinformation. The study also found that both systems provided similar translation for the same input due to either the use of similar MT approach or learning from previous translated texts. Moreover, both systems in some instances, achieve good results at the word level, but bad results at collocation units. The study also showed that automatic translation is insufficient for providing a full analysis of MT output because all automatic metrics are misleading due to dependence on text similarity to a reference human translation. For future research, the study recommended conducting a correlative study that combines manual and automatic evaluation methods to ensure best analysis of MT output. Machine Translation (MT) is still far from reaching fully automatic translation of a quality obtained by human translators.