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Journals / Language Teaching and Educational Research / 2026 / Volume: 9 Issue: 1

When Correct Isn’t Fluent: Translationese in Turkish-to-English Academic Writing and Its Implications for EAP

Mustafa Dolmacı(SELÇUK ÜNİVERSİTESİ)0000-0002-2503-6072
Pages
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Publication Date
2026-09-10
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

For English Language Teaching, particularly academic writing instruction for Turkish-speaking researchers, these findings suggest that raising awareness of specific interference patterns may serve learners better than generic fluency advice, and that quality-estimation tools could support self-editing before submission.

📌 3 Key Takeaways

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Objective & Hypothesis:Turkish scholars, like many non-native English speakers, routinely translate manuscripts written in their first language before submitting them to international journals, a practice that leaves linguistic traces even in grammatically sound texts.
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Key Findings & Data:This study examines that residue, commonly termed translationese, in the introductions of 40 bilingual articles from Education and Science, an SSCI- and Scopus-indexed Turkish education journal, using a reference-free neural quality-estimation metric (COMET-QE) to score each English introduction against its Turkish source.
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Conclusion & Impact:For English Language Teaching, particularly academic writing instruction for Turkish-speaking researchers, these findings suggest that raising awareness of specific interference patterns may serve learners better than generic fluency advice, and that quality-estimation tools could support self-editing before submission.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:translationese · academic writing · English for Academic Purposes

Abstract

Turkish scholars, like many non-native English speakers, routinely translate manuscripts written in their first language before submitting them to international journals, a practice that leaves linguistic traces even in grammatically sound texts. This study examines that residue, commonly termed translationese, in the introductions of 40 bilingual articles from Education and Science, an SSCI- and Scopus-indexed Turkish education journal, using a reference-free neural quality-estimation metric (COMET-QE) to score each English introduction against its Turkish source. After excluding five articles for alignment issues, scores across the remaining 35 clustered within a comparatively narrow band (M = 0.68), suggesting source-language interference is pervasive rather than occasional in this corpus. Contrary to expectation, neither how much a translation expanded relative to its source nor its raw length showed a significant relationship with its score. Close analysis of the lowest- and highest-scoring texts instead pointed to specific choices, such as calque, repetitive noun-phrase reference in place of pronouns, and clause structures that preserved Turkish syntax at English's expense, as more reliable markers of quality than any global text property. For English Language Teaching, particularly academic writing instruction for Turkish-speaking researchers, these findings suggest that raising awareness of specific interference patterns may serve learners better than generic fluency advice, and that quality-estimation tools could support self-editing before submission.

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Cite This Article

Dolmacı, M. (2026). When Correct Isn’t Fluent: Translationese in Turkish-to-English Academic Writing and Its Implications for EAP. Language Teaching and Educational Research, Volume: 9 Issue: 1. https://doi.org/10.35207/later.2004870

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References (28)

  1. Acı, M., Vuran Sarı, N., & İnan Acı, Ç. (2025). Morphological and structural complexity analysis of low-resource English-Turkish language pair using neural machine translation models. *PeerJ Computer Science, 11*, e3072. https://doi.org/10.7717/peerj-cs.3072
  2. Ackermann, K., & Chen, Y.-H. (2013). Developing the Academic Collocation List (ACL) – A corpus-driven and expert-judged approach. *Journal of English for Academic Purposes, 12*(4), 235–247. https://doi.org/10.1016/j.jeap.2013.08.002
  3. Baker, M. (1993). Corpus linguistics and translation studies: Implications and applications. In M. Baker, G. Francis, & E. Tognini-Bonelli (Eds.), *Text and technology: In honour of John Sinclair* (pp. 233–252). John Benjamins. https://doi.org/10.1075/z.64
  4. Baroni, M., & Bernardini, S. (2006). A new approach to the study of translationese: Machine-learning the difference between original and translated text. *Literary and Linguistic Computing, 21*(3), 259–274. https://doi.org/10.1093/llc/fqi039
  5. Casal, J. E., & Lee, J. J. (2019). Syntactic complexity and writing quality in assessed first-year L2 writing. *Journal of Second Language Writing, 44*, 51–62. https://doi.org/10.1016/j.jslw.2019.03.005
  6. Cavalin, P., Domingues, P. H., & Pinhanez, C. (2025). Sentence-level aggregation of lexical metrics correlates stronger with human judgements than corpus-level aggregation. *Proceedings of the AAAI Conference on Artificial Intelligence, 39*(22), 23532–23540. https://doi.org/10.1609/aaai.v39i22.34522
  7. Chesterman, A. (2004). Hypotheses about translation universals. In G. Hansen, K. Malmkjær, & D. Gile (Eds.), *Claims, changes and challenges in translation studies: Selected contributions from the EST Congress, Copenhagen 2001* (pp. 1–13). John Benjamins.
  8. Dogru, G., & Moorkens, J. (2024). Data augmentation with translation memories for desktop machine translation fine-tuning in 3 language pairs. *The Journal of Specialised Translation*, (41), 149–178. https://doi.org/10.26034/cm.jostrans.2024.4716
  9. Duan, W., Ng, C. F., Abdul Halim, H., & Zhang, Z. (2026). Evaluating machine translation of the Shan Hai Jing: An MQM-based analysis of Google Translate vs. ChatGPT with prompting effects. *GEMA Online Journal of Language Studies, 26*(2). https://doi.org/10.17576/gema-2026-2602-08
  10. Flowerdew, J. (2022). Models of English for research publication purposes. *World Englishes, 41*, 571–583. https://doi.org/10.1111/weng.12606
  11. Karpina, O., & Zasiekin, S. (2025). Representing emotive discourse in Ukrainian-English literary translation: A multi-method performance evaluation of Large Language Models, Neural Machine Translation and Computer-Assisted Translation tools. *East European Journal of Psycholinguistics, 12*(2), 178–203. https://doi.org/10.29038/kar
  12. Kepler, F., Trénous, J., Treviso, M., Vera, M., & Martins, A. F. T. (2019). OpenKiwi: An open-source framework for quality estimation. In *Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations* (pp. 117–122). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-3020
  13. Kunilovskaya, M., Morgoun, N., & Pariy, A. (2018). Learner vs. professional translations into Russian: Lexical profiles. *Translation & Interpreting, 10*(1), 33–52. https://doi.org/10.12807/ti.110201.2018.a03
  14. Liu, K., & Afzaal, M. (2021). Syntactic complexity in translated and non-translated texts: A corpus-based study of simplification. *PLoS ONE, 16*(6), e0253454. https://doi.org/10.1371/journal.pone.0253454
  15. Liu, Y., Zhang, W., Wang, Y., Tang, J., Zhang, P., Yang, B., Huang, F., Wang, R., & Hu, H. (2025). Translationese-index: Using likelihood ratios for graded and generalizable measurement of translationese. In *Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing* (pp. 12521–12538). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-main.633
  16. Öztürk, Y., & Taşçı, S. (2023). A corpus-based analysis of lexical bundles in non-native post-graduate academic writing and a potential L1 influence. *rEFLections, 30*(2), 488–505.
  17. Rei, R., Stewart, C., Farinha, A. C., & Lavie, A. (2020). COMET: A neural framework for MT evaluation. In *Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)* (pp. 2685–2702). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.213
  18. Rei, R., de Souza, J. G. C., Alves, D. M., Zerva, C., Farinha, A. C., Glushkova, T., Lavie, A., Coheur, L., & Martins, A. F. T. (2022a). COMET-22: Unbabel-IST 2022 submission for the metrics shared task. In *Proceedings of the Seventh Conference on Machine Translation* (pp. 578–585). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.wmt-1.52
  19. Rei, R., Treviso, M., Guerreiro, N. M., Zerva, C., Farinha, A. C., Maroti, C., de Souza, J. G. C., Glushkova, T., Alves, D. M., Lavie, A., Coheur, L., & Martins, A. F. T. (2022b). CometKiwi: IST-Unbabel 2022 submission for the quality estimation shared task. In *Proceedings of the Seventh Conference on Machine Translation* (pp. 634–645). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.wmt-1.60
  20. Rei, R., Guerreiro, N. M., Treviso, M., Lavie, A., Coheur, L., & Martins, A. F. T. (2023). The inside story: Towards better understanding of machine translation neural evaluation metrics. In *Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)* (pp. 1089–1105). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-short.94
  21. Şahin, M., & Dungan, N. (2014). Translation testing and evaluation: A study on methods and needs. *Translation & Interpreting: The International Journal of Translation and Interpreting Research, 6*(2), 67–90. https://doi.org/10.12807/ti.106202.2014.a05
  22. Sai, A. B., Dixit, T., Nagarajan, V., Kunchukuttan, A., Kumar, P., Khapra, M. M., & Dabre, R. (2023). IndicMT Eval: A dataset to meta-evaluate machine translation metrics for Indian languages. In *Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)* (pp. 14210–14228). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.795
  23. Schlam Salman, J., & Haskel-Shaham, I. (2023). Noticing and metalinguistic awareness in the L2 writing process: A translation-based writing task for L1 Arabic speakers preparing to teach Hebrew as a Second Language. *Journal of Second Language Writing, 62*, 101065. https://doi.org/10.1016/j.jslw.2023.101065
  24. Serova, O. U. (2025). An automated study of translationese indicators in scientific paper translations. *Terra Linguistica, 16*(1), 61–81. https://doi.org/10.18721/JHSS.16105
  25. Shen, B., & Bai, B. (2026). Explorations into EFL learners’ self-regulated writing strategy use in writing for scholarly publications: A case study of Chinese PhD students. *Language Awareness, 35*(3), 792–813. https://doi.org/10.1080/09658416.2025.2585007
  26. Toury, G. (2012). *Descriptive translation studies and beyond* (Rev. ed.). John Benjamins. https://doi.org/10.1075/btl.100
  27. Venuti, L. (2008). *The translator’s invisibility: A history*. Routledge. https://doi.org/10.4324/9780203360064
  28. Wang, Z., Cheung, A. K. F., & Liu, K. (2024). Entropy-based syntactic tree analysis for text classification: A novel approach to distinguishing between original and translated Chinese texts. *Digital Scholarship in the Humanities, 39*(3), 984–1000. https://doi.org/10.1093/llc/fqae030