فهرس المقالات Translation Quality Assessment (TQA)

• حرية الوصول المقاله
  • صفحة الملخص
  • نص كامل
  
  1 - Assessing the Performance Quality of Google Translate in Translating English and Persian Newspaper Texts Based on the MQM-DQF Model
  Zahra Foradi Jalilollah Faroughi Mohammad Reza Rezaeian Delouei
  10.30495/ttlt.2022.696826
  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 أکثر

  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. تفاصيل المقالة
• حرية الوصول المقاله
  • صفحة الملخص
  • نص كامل
  
  2 - Investigating Covert and Overt Errors Using Machine Translation according to House’s (2015) TQA Model within Academic Context
  Mohammad Iman Askari Adnan Satariyan Mahsa Ranjbar
  10.30495/ttlt.2021.683477
  The current study investigated Persian-English translations conducted by a human translator and a machine translator. The researchers employed House’s Translation Quality Assessment (TQA) model to evaluate the differences between the two translated works. Accordin أکثر

  The current study investigated Persian-English translations conducted by a human translator and a machine translator. The researchers employed House’s Translation Quality Assessment (TQA) model to evaluate the differences between the two translated works. Accordingly, they had the Persian texts translated by a human translator and Google Machine Translator (GMT). The translation quality, error recognition, and mismatches of the two translations were subsequently analyzed. The results showed a one-to-one match between the source and target texts regarding the human translator’s work. Furthermore, the results revealed both overt and covert errors when comparing the human and machine translators. The error analysis results also suggested that the quality of the output provided by the GMT can cause misunderstanding in the meaning. Academic texts could mean different in various contexts. Hence, it is necessary to consider human interferences when dealing with the genre of the academic text. تفاصيل المقالة