Google Translate and Microsoft Bing Translator’s Challenges in Rendering Camus’s The Stranger from English to Persian

Document Type : Research Paper

Authors

Department of English Language, Chabahar Maritime University, Chabahar, Iran

10.22111/ijals.2025.50602.2487

Abstract

Machine translation (MT) of literary texts presents unique challenges due to their stylistic complexity and cultural nuances. This study evaluated the performance of Google Translate (GT) and Microsoft Bing Translator (MBT) in translating Camus’s The Stranger from English to Persian. Data collection for this study involved automated evaluation using the Bilingual Evaluation Understudy (BLEU) metric and human evaluation conducted by three experts using the Localization Industry Standards Association (LISA) rubric. The results showed that GT significantly outperformed MBT across nearly all dimensions. GT achieved a BLEU score of 21.57 compared to MBT’s 6.36, with superior n-gram precision at all levels. The human evaluation phase also revealed GT’s fewer critical and major errors in almost all categories compared to MBT. However, both systems struggled to preserve the aesthetic and philosophical richness of The Stranger. These findings highlight the persistent limitations of MT in literary translation, particularly for linguistically distant pairs like English and Persian. While MT shows potential as a supplementary tool, it remains unsuitable as a replacement for human translators in capturing the depth and artistry of literary works.

Keywords

References

Baker, M. (2018). In other words: A coursebook on translation. Routledge. https://doi.org/10.4324/9781315619187.
Camus, A. (1989). The stranger (M. Ward, Trans.). First Vintage International Edition. Random House. (Original work published 1942).
Castilho, S., & Resende, N. (2022). Post-editese in literary translations. Information, 13(2), 66. https://doi.org/10.20944/preprints202112.0117.v1.
Cui, S. (2021). Aesthetic characteristics and artistic value of English literature translation in a multimodal environment. In 2021 2nd International Conference on Computers, Information Processing and Advanced Education (pp. 587–590). https://doi.org/10.1145/3456887.3457020.
Delabastita, D. (2010). Histories and utopias: On Venuti’s the translator’s invisibility. The Translator, 16(1), 125–134. https://doi.org/10.1080/13556509.2010.10799296.
Delabastita, D. (2019). Fictional representations. In Routledge Encyclopedia of Translation Studies (pp. 189–194). Routledge. https://doi.org/10.4324/9781315678627-41.
Deyhimi, K. (2009). The stranger (K. Deyhimi, Trans.). Nashr-e Mahi. (Original work published 1942).
Dorst, A. G. (2024). Metaphor in literary machine translation: Style, creativity, and literariness. In Computer-Assisted Literary Translation (pp. 173–186). Routledge. https://doi.org/10.4324/9781003357391-12.
Gao, R., Lin, Y., Zhao, N., & Cai, Z. G. (2024). Machine translation of Chinese classical poetry: a comparison among ChatGPT, Google Translate, and DeepL Translator. Humanities and Social Sciences Communications, 11(1), 1–10. https://doi.org/10.1057/s41599-024-03363-0.
Guerberof-Arenas, A., & Toral, A. (2022). Creativity in translation: Machine translation as a constraint for literary texts. Translation Spaces, 11(2), 184–212. https://doi.org/10.1075/ts.21025.gue.
Hatim, B., & Mason, I. (2014). Discourse and the translator. Routledge. https://doi.org/10.4324/9781315846583.
Jibreel, I. (2023). Online machine translation efficiency in translating fixed expressions between English and Arabic (proverbs as a case-in-point). Theory and Practice in Language Studies, 13(5), 1148–1158. https://doi.org/10.17507/tpls.1305.07.
Jones, R., & Irvine, A. (2013). The (un) faithful machine translator. In Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (pp. 96–101). https://aclanthology.org/W13-2713.
Karabayeva, I., & Kalizhanova, A. (2024). Evaluating machine translation of literature through rhetorical analysis. Journal of Translation and Language Studies, 5(1), 1–9. https://doi.org/10.48185/jtls.v5i1.962.
Karpinska, M., & Iyyer, M. (2023). Large language models effectively leverage document-level context for literary translation, but critical errors persist. arXiv preprint arXiv:2304.03245. https://doi.org/10.18653/v1/2023.wmt-1.41.
Katan, D., & Taibi, M. (2021). Translating cultures: An introduction for translators, interpreters, and mediators. Routledge. https://doi.org/10.4324/9781003178170.
Kuzman, T., Vintar, Š., & Arcan, M. (2019). Neural machine translation of literary texts from English to Slovene. In Proceedings of the Qualities of Literary Machine Translation (pp. 1–9). https://aclanthology.org/W19-7301.
Landers, C. E. (2001). Literary translation: A practical guide. Multilingual Matters. https://compress-pdf-free.obar.info.
Matusov, E. (2019). The challenges of using neural machine translation for literature. In Proceedings of the Qualities of Literary Machine Translation (pp. 10–19). https://aclanthology.org/W19-7302.
Munday, J., Pinto, S. R., & Blakesley, J. (2022). Introducing translation studies: Theories and applications. Routledge. https://doi.org/10.4324/9780429352461-6.
Naveen, P., & Trojovský, P. (2024). Overview and challenges of machine translation for contextually appropriate translations. Iscience, 27(10). https://doi.org/10.1016/j.isci.2024.110878.
Newmark, P. (1988). Pragmatic translation and literalism. TTR: Traduction, Terminologie, Rédaction, 1(2), 133–145. https://doi.org/10.7202/037027ar.
Nord, C. (2014). Translating as a purposeful activity: Functionalist approaches explained. Routledge. https://doi.org/10.4324/9781315760506.
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: A method for automatic evaluation of machine translation. In P. Isabelle, E. Charniak, & D. Lin (Eds.), Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (pp. 311–318). Association for Computational Linguistics. https://doi.org/10.3115/1073083.1073135.
Snell-Hornby, M. (2006). The turns of translation studies: New paradigms or shifting viewpoints? John Benjamins Publishing. https://doi.org/10.1075/btl.66.
Taivalkoski-Shilov, K. (2019). Ethical issues regarding machine (-assisted) translation of literary texts. Perspectives, 27(5), 689–703. https://doi.org/10.1080/0907676x.2018.1520907.
Thai, K., Karpinska, M., Krishna, K., Ray, B., Inghilleri, M., Wieting, J., & Iyyer, M. (2022). Exploring document-level literary machine translation with parallel paragraphs from world literature. arXiv preprint arXiv:2210.14250. https://doi.org/10.18653/v1/2022.emnlp-main.672.
Toral, A., & Way, A. (2015). Machine-assisted translation of literary text: A case study. Translation Spaces, 4(2), 240–267. https://doi.org/10.1075/ts.4.2.04tor.
Toral, A., Van Cranenburgh, A., & Nutters, T. (2024). Literary-adapted machine translation in a well-resourced language pair: Explorations with More Data and Wider Contexts. In Computer-Assisted Literary Translation (pp. 27–52). Routledge. https://doi.org/10.4324/9781003357391-3.
Van Egdom, G. W., Kosters, O., & Declercq, C. (2023). The riddle of (literary) machine translation quality. Tradumàtica Tecnologies de la Traducció, (21), 129–159. https://doi.org/10.5565/rev/tradumatica.345.
Vinay, J. P., & Darbelnet, J. (2000). A methodology for translation. In L. Venuti (Ed.), The Translation Studies Reader (pp. 84–93). Routledge. https://translationjournal.net/images/e-Books/PDF_Files/The%20Translation%20Studies%20Reader.pdf.
Voigt, R., & Jurafsky, D. (2012, June). Towards a literary machine translation: The role of referential cohesion. In D. Elson, A. Kazantseva, R. Mihalcea, & S. Szpakowicz (Eds.), Proceedings of the NAACL-HLT 2012 Workshop on Computational Linguistics for Literature (pp. 18–25). Association for Computational Linguistics. https://aclanthology.org/W12-2503/.
Wang, Q. (2021). An investigation of challenges in machine translation of literary texts: The case of the English–Chinese language pair (Master’s thesis). Western Sydney University. https://researchdirect.westernsydney.edu.au/islandora/object/uws%3A67814.
Wittman, E. O. (2013). Literary narrative prose and translation studies. In C. Millán & F. Bartrina (Eds.), The Routledge Handbook of Translation Studies (pp. 438–450). Routledge. https://doi.org/10.4324/9780203102893-43.
Zahroh, H., Basid, A., & Jumriyah, J. (2023). Comparison results of Google Translate and Microsoft Translator on the novel Mughamarah Zahrah Ma'a Ash-Syajarah by Yacoub Al-Sharouni. Al-Lisan: Jurnal Bahasa (e-Journal), 8(2), 154–170. https://doi.org/10.30603/al.v8i2.3675.
Iranian Journal of Applied Language Studies
Volume 17, Issue 2
October 2025
Pages 101-120

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