Assessing the Quality of Machine Translation from Kurmanji Kurdish into English


  • Hakar Hazim M. Ameen Department of Translation, University of Duhok, Iraq-Kurdistan, Iraq
  • Hussein Ali Ahmed Department of Translation. Nawroz University, Iraq-Kurdistan, Iraq



The assessment of quality by the current most widely used on-line machine translation systems such as Google Translate and Bing Translator has always been a hotly debated and controversial topic.  This research endeavors to assess the translation quality of the already referred to on-line machine translation systems so as to highlight the level of their inadequate quality, if any. Yet, due to the nonexistence of a unique quality assessment method as far as the translation by the two systems is concerned, the current research sets out to utilize an error analysis method for assessing the quality of the translation of two specialized texts from Kurdish into English by Google Translate and Bing Translator systems. The error analysis of the chosen texts reveals that both systems achieved excellent results in the orthography category, with 100 and 98.7 percent accuracy for Google and Bing, respectively. Additionally, results of 98.8% for Google and 97.5% for Bing concerning  lexis reflected positive outcomes for both systems. Because both systems recently adopted NMT (neural machine translation), which simulates the way human brain functions to produce translation and learns from texts formerly translated by human translators, the two systems performed very well in these areas. The analysis also shows that the two selected systems were  successful in the translation of the selected texts with reference to English rules of grammar achieving outstanding results that are 99.6 accuracy for Google and 99.4 for Bing. For further research, this study recommends doing more assessment on translation of more types of Kurdish texts through conducting the linguistic error analysis.


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Alawneh, M. F., & Sembok, T. M. (2011, September). Rule-based and example-based machine translation from English to Arabic. In 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications (pp. 343-347). IEEE.

Almahasees, Z., & Mustafa, Z. (2017). Machine Translation Quality of Khalil Gibran's the Prophet. AWEJ for translation & Literary Studies Volume, 1.

Ashby, M. (2000). Oxford advanced learner's dictionary of current English.

Balkul, H. I. (2018). A comparative analysis of translation/interpreting tools developed for Syrian refugee crisis. International Journal of Language Academy, 6(4), 32-44.

Condon, S., Parvaz, D., Aberdeen, J., Doran, C., Freeman, A., & Awad, M. (2010). Evaluation of machine translation errors in English and Iraqi Arabic. MITRE CORP MCLEAN VA.

Costa, Â., Ling, W., Luís, T., Correia, R., & Coheur, L. (2015). A linguistically motivated taxonomy for Machine Translation error analysis. Machine Translation, 29(2), 127-161.

Crosson, F. J. (Ed.). (1970). Human and artificial intelligence. Ardent Media.

Daniele, F. (2019). Performance of an automatic translator in translating medical abstracts. Heliyon, 5(10), e02687.

Datta, D., David, P. E., Mittal, D., & Jain, A. (2020). Neural machine translation using recurrent neural network. International Journal of Engineering and Advanced Technology, 9(4), 1395-1400.

Delavenay, E. (1960). An introduction to machine translation (p. 90). London: Thames and Hudson.

Dorr, B., Snover, M., & Madnani, N. (2011). Part 5: machine translation evaluation. Handb. Nat. Lang. Process. Mach. Transl. DARPA Glob. Auton. Lang. Exploit, 936.

Forcada, M. L., Ginestí-Rosell, M., Nordfalk, J., O’Regan, J., Ortiz-Rojas, S., Pérez-Ortiz, J. A., ... & Tyers, F. M. (2011). Apertium: a free/open-source platform for rule-based machine translation. Machine translation, 25(2), 127-144.

Google Translate. Google Inc. Retrieved April 4, 2022

Groves, M., & Mundt, K. (2015). Friend or foe? Google Translate in language for academic purposes. English for Specific Purposes, 37, 112-121.

Hannouna, Y. H. A. A. H. (2004). Evaluation of machine translation systems: The translation quality of three Arabic systems. Unpublished phd dessertation.

Harper, K. E. (1957). Contextual analysis. Mech. Transl. Comput. Linguistics, 4(3), 70-75.

Hartley, T. (2009). Technology and translation. In The Routledge companion to translation studies (pp. 120-141). Routledge.

Hassan, H., Aue, A., Chen, C., Chowdhary, V., Clark, J., Federmann, C., ... & Zhou, M. (2018). Achieving human parity on automatic chinese to english news translation. arXiv preprint arXiv:1803.05567.

Hovy, E., King, M., & Popescu-Belis, A. (2002). Principles of context-based machine translation evaluation. Machine Translation, 17(1), 43-75.

Hutchins, J. & Somers, H. L. (1992). An Introduction to Machine Translation.

Hutchins, J. (1986) Machine translation: past, present, future. Ellis Horwood, Chichester, UK. (Halstead Press, New York)

Jurafsky, D., & Martin, J. H. (2009). Speech and Language Processing, 2nd edn Upper Saddle River.

Kaka-Khan, K. M. (2018). English to Kurdish Rule-based Machine Translation System. UHD Journal of Science and Technology.

Kaplan, A. (1955). An experiment study of ambiguity and context. Mechanical Translation, 2, 39-46.

Koutsoudas, A., & Korfhage, R. (1956). Mechanical Translation and the Problem of Multiple Meaning. In Proceedings of the International Conference on Mechanical Translation.

Lee, S. M. (2020). The impact of using machine translation on EFL students’ writing. Computer Assisted Language Learning, 33(3), 157-175.

LISA. Accessed July 2010. “Home page of the Localization Industry Standards Association”.

MacKenzie, D. N. (1961). 1962. Kurdish Dialect Studies, 1, 2.

McArthur, T. (1992). The Oxford Companion to the English Language. Oxford University Press. New York.

Microsoft Translator- Translator Text API". Microsoft. Archived from the original on April 9, 2022. Translator Text API - Microsoft Translator for Business (

Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002, July). Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics (pp. 311-318).

Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002, July). Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics (pp. 311-318).

Pérez, C. R. (2001). From novelty to ubiquity: computers and translation at the close of the industrial age. Translation Journal, 5(1).

Popović, M., & Ney, H. (2007, June). Word error rates: Decomposition over POS classes and applications for error analysis. In Proceedings of the Second Workshop on Statistical Machine Translation (pp. 48-55).

Sinclair, J. (1992). BBC English dictionary. HaperCollins Publishers.

Sjahrony, K., & Ahmad, M. (2013). Penterjemahan Frasa Al-Idafah Arab-Melayu Menggunakan Google Translate. Islamiyyat: International Journal of Islamic Studies, 35(2).

Summers, D., & Stock, P. (Eds.). (1993). Longman dictionary of English language and culture. Harlow, England: Longman.

Taher, F. J. & Kaka-Khan, K. M., (2017). Evaluation of inkurdish Machine Translation System. Journal of University of Human Development, 3(2), 862-868.

Taher, F. J. (2017). Evaluation of inkurdish Machine Translation System. Journal of University of Human Development, 3(2), 862-868.

Talaván Zanón, N. (2005). Evaluating the output quality of machine translation systems: systran 4.0. Philologia hispalensis, 19, 189-201.

Thackston, W. M. (2006). Kurmanji Kurdish:-A Reference Grammar with Selected Readings. Renas Media.

Toma, P. (1976). An operational machine translation system. Translation: Applications and research, Gardner Press, New York, NY, 247-259.

Turovsky, B. (April 28, 2016). "Ten years of Google Translate". Google Translate Blog. Google Inc. Retrieved December 24, 2019.

Ulatus. (April 8, 2020). The Usefulness of Translation Apps. Retrieved from Translations Made Simple: The Usefulness of Translation Apps – Ulatus

Wendt, C. (2010). Better translations with user collaboration–integrated MT at Microsoft. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Commercial MT User Program.

Wilks, Y. A. (1972). Grammar, meaning and the machine analysis of language. London: Routledge & Kegan Paul.



How to Cite

Hazim M. Ameen, H., & Ali Ahmed, H. (2023). Assessing the Quality of Machine Translation from Kurmanji Kurdish into English. Academic Journal of Nawroz University, 12(3), 503–517.