Saharnaz Saberi, - MA student in Translation Studies, Zand Institute of Higher Education,Shiraz Iran
Mortaza Yamini - ۲Assistant Professor of TEFL, Department of English Language, Zand Institute of Higher Education,Shiraz,Iran;
Maryam Sharif - Assistant Professor of TEFL, Department of English Language, Naghshejahan Higher Education Institute, Isfahan,Iran

One of the most commonly used Machine Translation (MT) systems is Google Translate, which supports ۶۴ languages, including Persian. As Google Translate is easily accessible, it is almost always the first MT system to which Iranian users resort to meet their translation needs. This study attempted to investigate how Google translates homographic homophones, in the first place to reach the statistical results about Google translator engine accuracy in translating homographic homophones. Thus, calculating the percentage of successful translation of homographic homophones by Google Translate was one major objective of the study. Also, this study aimed to determine whether Google Translate was trustworthy in translating homographic homophones. To fulfill this objective, descriptive qualitative methods were selected. The corpus was extracted from Mo’in Encyclopedic Dictionary (۲۰۱۵) which consisted of ۱۱۴ selected homographic homophones. To evaluate the translation quality of the homographic homophones in terms of accuracy and truth worthiness, the translation quality framework proposed by Nababan, Nuraeni and Sumardiono (۲۰۱۲) was adopted. Descriptive statistics containing frequency and percentage were calculated. The results showed out of ۱۱۴ predetermined cases of the words, only ۶% were translated accurately and in complete correspondence with the real translation of the intended words in different contexts. However, ۶۴.۹% of the translated words were found to be inaccurate and incompatible with the intended meaning of the given context. Also, among ۲۶۰ different contexts in which the intended homographic homophones were used, Google Translate could only differentiate between the semantics of ۲۱.۱۵% of homographic homophones

Machine translation, Homographic homophones, Quality, Google Translate