Assessing the Performance Quality of Google Translate in Translating English and Persian Newspaper Texts Based on the MQM-DQF Model

چکیده مقاله

The use of machine translation to communicate and access information has become increasingly common. Various translation software and systems appear on the Internet to enable interlingual communication. Accelerating translation and reducing its cost are other factors in the increasing popularity of machine translation. Even if the quality of this type of translation is lower than human translation, it is still significant in many ways. The MQM-DQF model provides standards of error typology for objective and quantitative assessment of translation quality. In this model, two criteria (accuracy and fluency) are used to assess machine translation quality. The MQM-DQF model was used in this study to assess the quality of Google Translate performance in translating English and Persian newspaper texts. Five texts from Persian newspapers and five texts from English newspapers were randomly selected and translated by Google Translate both at the sentence level and the whole text. The translated texts were assessed based on the MQM-DQF model. Translation errors were identified and coded at three severity levels: critical, major, and minor errors. By counting the errors and scoring them, the percentage of accuracy and fluency criteria in each of the translated texts was calculated. The results showed that Google Translate performs better in translating texts from Persian into English; furthermore, in sentence-level translation, it performs better than the translation of the whole text. Moreover, translations of different newspaper texts (economic, cultural, sports, political, and scientific) were not of the same quality.