Investigating Translation Problems in Persian to English Translation of Literary Texts by Google Translate based on Nord’s Model

This study was aimed at investigating Persian to English translation of literary texts by Google Translate based on Nord’s (2001) model. To this aim, a mixed method approach was used. Corpus of this study included 89 pages which were randomly selected from Persian literary books available in the market and some literary texts from the electronic websites along with their English translation by Google Translate. To analyze the collected data, the corpus was subjected to qualitative comparative content analysis based on Nord’s model by two raters including the researcher and a friend who was an experienced translator. Results of data analysis revealed that there were 64 cases of linguistic translation problems and 8 cases of text-specific translation problems in the analyzed translations; linguistic translation problems were the most frequent problem types identified in the translations; text-specific translation problems were the second-most frequent translation problem types identified in the translations; though, no cultural and pragmatic translation problems were identified in the selected corpus. The results of this study could shed light on the implications of the introduction of technology into translation curricula and the probable challenges Machine Translation can introduce accordingly.

Investigating Translation Problems in Persian to English Translation of Literary Texts by Google Translate based on Nord’s Model Keywords:

Translation , Nord’s (2001) model of TQA , Google Translate.