Translating food idioms across the English-Arabic language divide: A comparative study of human translators, ChatGPT, and Gemini
Main Article Content
Abstract
Idiomatic expressions, especially those rooted in the universal domain of food, represent one of the most culture-embedded and translation-difficult forms of language. The current study aims to examine the translation of twenty English food idioms into Arabic and twenty Arabic food idioms into English by human professional translators, as well as two of the most prominent large language models (LLMs): ChatGPT (GPT-4o) and Google Gemini Advanced. A mixed-methods approach is used to evaluate the performance of all sixty translations of each idiom on four criteria: semantic equivalence (SE)1., pragmatic appropriateness (PA), cultural adaptation (CA), and natural fluency (NF), with a five-point scale for each. A composite translation quality score is derived for each translator/agent. The results show that human professional translators outperform the two LLMs on all criteria with a mean translation quality score of 5.00, compared to 3.83 for ChatGPT and 3.60 for Gemini. The results also show the greatest performance gap between human translators and the two LLMs is on cultural adaptation. The results of the current study have important implications for the pedagogy of translation, translation practice, and the development of culturally sensitive AI translation tools.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
You are free to: Share — copy and redistribute the material in any medium or format. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
No additional restrictions You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
How to Cite
Share
References
Abdou, A. (2011). Arabic idioms: A corpus based study. Routledge.
Ahmed, S. S. (2024). Translation and semantics: Challenges and strategies in translating english idioms. JOURNAL OF LANGUAGE STUDIES, 8(3), 335-347.
Al-Mannai, K., Sajjad, H., Khader, A., Al Obaidli, F., Nakov, P., & Vogel, S. (2014). Unsupervised word segmentation improves dialectal Arabic to English machine translation. Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP),
Al-Otaibi, G. M., Alotaibi, H. M., & Alsalmi, S. S. (2025). Communication Errors in Human–Chatbot Interactions: A Case Study of ChatGPT Arabic Mental Health Support Inquiries. Behavioral Sciences, 15(8), 1119.
Al-Rushaidi, S. M. S., & Ali, H. I. H. (2017). Translating food menus from English into Arabic: Linguistic and cultural dilemmas. Arab World English Journal for Translation and Literary Studies, 1(1), 201-212.
Alhabahba, M. M., Pandian, A., & Mahfoodh, O. H. A. (2016). English language education in Jordan: Some recent trends and challenges. Cogent Education, 3(1), 1156809.
Alhasnawi, S. (2024). Linguacultural variations in the domain of FOOD. World Englishes, 43(3), 414-438.
Alkhasawneh, S. (2026). Designing Culturally Responsive AI Tutors: Bridging Equity Gaps in Arab K–12 Education. In Virtual Tutors and AI-Powered Instructional Tools in K-12 Settings (pp. 289-326). IGI Global Scientific Publishing.
Almahasees, Z. (2021). Analysing English-Arabic Machine Translation: Google Translate, Microsoft Translator and Sakhr. Routledge.
Almahasees, Z., Al-Harahsheh, A., & Albudairi, Y. (2025). Assessing DeepSeek and ChatGPT in English-Arabic translation of political texts. Research Journal in Advanced Humanities, 6(1).
Almahasees, Z., Albudairi, Y., Al-Natour, M., AL-Harahsheh, A., & Mahmoud, S. (2025). Challenges and Strategies in Translating Legal Terms between English and Arabic: A Comparative Study of International Accords and Agreements. Forum for Linguistic Studies,
Almahasees, Z., Meqdadi, S., & Albudairi, Y. (2021). Evaluation of google translate in rendering English COVID-19 texts into Arabic. Journal of Language and Linguistic Studies, 17(4), 2065-2080.
Alomoush, O. (2015). Multilingualism in the linguistic landscape of urban Jordan University of Liverpool].
Baker, M. (2018). In other words: A coursebook on translation. Routledge.
Banerjee, S., & Lavie, A. (2005). METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization,
Behzad, S., Zeldes, A., & Schneider, N. (2024). To ask LLMs about English grammaticality, prompt them in a different language. Findings of the Association for Computational Linguistics: EMNLP 2024,
Boers, F. (2014). Idioms and phraseology. The Bloomsbury companion to cognitive linguistics, 185-202.
Ecenglish. (2025). 70 statistics about the English language. https://ecenglish.com/en/blog/news/70-statistics-about-the-english-language/
Eser, O., Lai, M., & Saltan, F. (2020). The affordances and challenges of wearable technologies for training public service interpreters. Interpreting, 22(2), 288-308.
Ferrag, F., & Bentounsi, I. (2024). The Use of Artificial Intelligence in Academic Translation Tasks Case Study of Chat GPT, Claude and Gemini. Ziglobitha,(2), 173-192.
Fitria, T. N. (2025). Revolutionizing English learning with AI: Insights from ChatGPT and Google Gemini. International Journal of Computer and Information System (IJCIS), 6(2), 77-91.
Frye, J., & Bruner, M. (2012). The rhetoric of food: Discourse, materiality, and power. Routledge.
Garzone, G. (2017). Food, culture, language and translation. Journal of Multicultural Discourses, 12(3), 214-221.
Gregov, E. (2018). Domestication and Foreignization in the Translation of Different Text Types
House, J. (2014). Translation quality assessment: Past and present. Routledge.
Keizer, E. (2016). Idiomatic expressions in Functional Discourse Grammar. Linguistics, 54(5), 981-1016.
Kemppanen, H., Jänis, M., & Belikova, A. (2012). Domestication and foreignization in translation studies (Vol. 46). Frank & Timme GmbH.
Kenny, D. (2019). Technology and translator training. The Routledge handbook of translation and technology, 498-515.
Kleidermacher, H. C., & Zou, J. (2025). Science across languages: assessing LLM multilingual translation of scientific papers. arXiv preprint arXiv:2502.17882.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, 159-174.
Laviosa, S. (2004). Corpus-based translation studies: Where does it come from? Where is it going? Language matters, 35(1), 6-27.
Liontas, J. I. (2019). Teaching idioms and idiomatic expressions across the second language curriculum. Teaching essential units of language: Beyond single-word vocabulary, 55-105.
Lommel, A., Uszkoreit, H., & Burchardt, A. (2014). Multidimensional quality metrics (MQM): A framework for declaring and describing translation quality metrics. Tradumàtica(12), 0455-0463.
Majić Mazul, K. (2017). Idiomatic expressions associated with the domain FOOD in English and their counterparts in Croatian Sveučilište Josipa Jurja Strossmayera u Osijeku, Sveučilište Josipa Jurja …].
Mansour, S., Al-Onaizan, Y., Blackwood, G., & Tillmann, C. (2014). Automatic dialect classification for statistical machine translation. Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track,
Meso, T. P. (2023). An analysis of the role of culture in the tradition of Northern Sotho idiomatic expressions into English
Milošević, J., & Risku, H. (2020). Situated cognition and the ethnographic study of translation processes: Translation scholars as outsiders, consultants and passionate participants. Linguistica Antverpiensia, New Series–Themes in Translation Studies, 19.
Mohammed, J. M. D.-A. (2025). Linguistic and cultural barriers in translating English idioms into Arabic. Journal of Ecohumanism, 4(2), 820-837.
Monti, J. (2015). Multi-word Unit Processing in Machine Translation. Developing and using language resources for multi-word unit processing in Machine Translation.
Nida, E. A. (2001). Dynamic equivalence in translating. An Encyclopedia of Translation. Chinese-English. English-Chinese, 223-230.
Ochieng, M., Gumma, V., Sitaram, S., Wang, J., Chaudhary, V., Ronen, K., Bali, K., & O’Neill, J. (2025). Beyond Metrics: Evaluating LLMs Effectiveness in Culturally Nuanced, Low-Resource Real-World Scenarios. Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025),
Papineni, K. (2002). Machine Translation Evaluation: N-grams to the Rescue. LREC,
Pym, A. (2014). Vinay and Darbelnet and the politics of translation solutions. Retrieved January, 14, 2017.
Pym, A. (2020). Translation, risk management and cognition. In The Routledge handbook of translation and cognition (pp. 445-458). Routledge.
Toury, G. (1999). Culture planning and translation. A: Álvarez, A. & A. Fernández (eds.) Anovar/Anosar. Estudios de Traducción e Interpretación. Vigo: Universidade, 13-25.
Vinay, J.-P., & Darbelnet, J. (1995). Comparative stylistics of French and English.
Yang, W. (2010). Brief study on domestication and foreignization in translation. Journal of Language Teaching and Research, 1(1), 77-80.
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019). Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675.