Assessing the Performance Quality of Google Translate in Translating English and Persian Newspaper Texts Based on the MQM-DQF Model
محورهای موضوعی : نشریه زبان و ترجمه
Zahra Foradi
1
(
Department of English Language and Literature, University of Birjand, Birjand, Iran
)
Jalilollah Faroughi
2
(
گروه زبان انگلیسی دانشگاه بیرجند
)
Mohammad Reza Rezaeian Delouei
3
(
Department of English Language and Literature, University of Birjand, Birjand, Iran
)
کلید واژه: Accuracy, Fluency, Translation Quality Assessment (TQA), Machine Translation (MT), 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.
استفاده از ترجمۀ ماشینی برای برقرای ارتباطات و دستیابی به اطلاعات به‌طور فزاینده‌ای رواج یافته است. تسریع در ترجمه و کاهش هزینۀ آن، از عوامل دیگر استقبال روزافزون در استفاده از ترجمۀ ماشینی است. حتی اگر کیفیت این نوع ترجمه کمتر از ترجمۀ انسانی باشد، باز هم از جنبه‌های گوناگون اهمیت آن چشمگیر است. مدل MQM-DQF برای ارزیابی عینی و کمّی کیفیت ترجمه و دوری از ذهنیت‌گرایی، استانداردهای خطا‌شناسى ارائه کرده است. در این مدل برای ارزیابی کیفیت ترجمۀ ماشینی از دو معیار دقت و سلاست استفاده می‌شود. در این تحقیق برای ارزیابی کیفیت عملکرد مترجم برخط گوگل در ترجمۀ متون روزنامه‌ای انگلیسی و فارسی، مدل MQM-DQF به کار گرفته شد. پنج متن از روزنامه‌های فارسی و پنج متن از روزنامه‌های انگلیسی کثیرالانتشار به‌طور تصادفی انتخاب و توسط مترجم بر خط گوگل هم در سطح جمله و هم به صورت کلی ترجمه شد. متن‌های ترجمه‌شده بر اساس معیارهای دقت و سلیس بودن بررسی و خطاهای ترجمه در سه سطح بحرانی، بزرگ و کوچک شناسایی و کدگذاری شد. با شمارش خطاها و نمره‌دهی به آنها، درصد معیارهای دقت و سلاست در هر یک از متن‌های ترجمه‌شده محاسبه گردید. نتایج تحقیق نشان داد که مترجم برخط گوگل در ترجمۀ متون فارسی به انگلیسی عملکرد بهتری نسبت به ترجمۀ متون انگلیسی به فارسی دارد و ترجمه در سطح جمله، کیفیت بالاتری نسبت به ترجمۀ کلی متون دارد. ترجمۀ انواع متن‌ها (اقتصادی، فرهنگی، ورزشی، سیاسی و علمی) از کیفیت یکسانی برخوردار نبود.
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