Cohesion-based Errors and Intelligibility of Persian-English Machine Translation Output
Keywords:
Machine translation, Translation engines, Translation errors, Intelligibility, TechnologyAbstract
Basically, nowadays, such translation-based computer technologies as machine translation systems are employed so as to increase the speed and achieve far higher quality in translation due to the high volume of translation projects and limited time periods. The main goal of this research is to identify the cohesion- based errors committed in machine translation, to evaluate their impact upon the chains of the cohesive devices and also upon the intelligibility of machine translated outputs. In this research, the data was collected from three different machine translation systems, followed by 30 master students of TEFL as the participants in this research were employed in order to back translate a selected quantity of machine translated texts so as to evaluate the quality and the level of intelligibility. Based on the obtained results, it was determined that Google machine translation system assigned the first position due to its better quality and the less proportion of cohesion-based errors committed. On the other hand, Abadis, with a relatively small distinction compared to Google's performance in terms of the quality and the statistic of committed errors, allocated the second position, which confirmed its far better performance than Bing. In addition, regarding the cohesion-based errors affecting the intelligibility of the machine translated texts, it could be argued that among the six types of cohesion-based errors identified, the missing-word error and the non-translated word error were taken into account with the highest and the lowest impact upon the participants' intelligibility of the machine translated texts.
Downloads
References
1. Forcada ML. Machine translation today In - Handbook of Translation Studies: Volume 1 (pp. 215-223). John Benjamins Publishing Company; 2012.
2. Wang H, Wu H, He Z, Huang L, Church KW. Progress in machine translation. Engineering. 2022;18:143-53. doi: 10.1016/j.eng.2021.03.023.
3. Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, et al., editors. Google's neural machine translation system: Bridging the gap between human and machine translation2016.
4. Al-Jarf R. Google Translate then and now: Translations from five languages into English and Arabic (2012-2025). Journal of Computer Science and Technology Studies. 2025;7(12):413-27. doi: 10.32996/jcsts.2025.7.12.50.
5. Mirzaeian VR. The effect of editing techniques on machine translation-informed academic foreign language writing. The EuroCALL Review. 2021;29(2):33-43. doi: 10.4995/eurocall.2021.12930.
6. Hardmeier C, editor On statistical machine translation and translation theory In - Proceedings of the Second Workshop on Discourse in Machine Translation (pp. 168-172)2015.
7. Deng Y. Machine Translation in EFL Learning: Chinese Undergraduates' Use, Perceptions, and Machine Translation Literacy. J Electrical Systems. 2024;20(7s):204-16. doi: 10.52783/jes.3263.
8. Yuasa M, Takeuchi O. Strategic use of machine translation: A case study of Japanese EFL university students. AILA Review. 2024;37(2):215-40. doi: 10.1075/aila.24020.yua.
9. Yusof NM, Darus S, Ab Aziz MJ. Evaluating Intelligibility in Human Translation and Machine Translation. 3L, Language, Linguistics, Literature. 2017;23(4). doi: 10.17576/3L-2017-2304-19.
10. Halliday MAK, Hasan R. Cohesion in English: Longman; 1976.
11. Askarieh S, editor Cohesion and Comprehensibility in Swedish-English Machine Translated Texts2014.
12. Weiss S. Cohesion and comprehensibility in Polish-English machine translated texts: Unpublished Master Thesis, Linkoping University, Sweden; 2011.
13. Saffari M, Sajjadi S, Mohammadi M. Evaluation of machine translation (Google Translate vs. Bing Translator) from English into Persian across academic fields. Modern Journal of Language Teaching Methods. 2017;7(8):429-42.
14. Aslerasouli P, Abbasian GR. Comparison of Google Online Translation and Human Translation with regard to Soft & Hard science Texts. Journal of Applied Linguistics and Language Research. 2015;2(3):169-84.
15. Bawden R. Going beyond the sentence: Contextual machine translation of dialogue: Université Paris Saclay (COmUE); 2018.
16. Guillou LK. Incorporating pronoun function into statistical machine translation. 2016.
17. Bowker L. Towards a methodology for a corpus-based approach to translation evaluation. Meta. 2001;46(2):345-64. doi: 10.7202/002135ar.
18. Matsuzaki T, Fujita A, Todo N, Arai NH, editors. Translation errors and incomprehensibility: A case study using machine-translated second language proficiency tests In - Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) (pp. 2771-2776)2016.
19. Chorowski J, Weiss RJ, Bengio S, Van Den Oord A. Unsupervised speech representation learning using wavenet autoencoders. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2019;27(12):2041-53. doi: 10.1109/TASLP.2019.2938863.
20. Abdi H. Machine Translation Quality Evaluation and Post-Editing Efficiency: The Case of Abadis Translator. k@ ta: A Biannual Publication on the Study of Language and Literature. 2025;27(1):18-33. doi: 10.9744/kata.27.1.18-33.
21. Pshenichnikov D. Key Challenges and Professional Tasks in the Field of Machine Translation Post-Editing. Universal Library of Languages and Literatures. 2024;1(1).
22. Creswell JW, Plano Clark VL. Revisiting mixed methods research designs twenty years later In - Handbook of mixed methods research designs (pp. 21-36). 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Sadegh Alizadeh Tehrani
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
