Addressing Untranslatability: ChatGPT’s Compensatory Strategies for Translating Verbified Proper Nouns in English–Arabic Contexts

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Published: Jun 30, 2025
DOI: https://doi.org/10.52919/translang.v24i01.1037
Keywords:
ChatGPT, AI translation, Denominalization, Untranslatability, Compensation, Newmark, Arabic patterning

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Darawsheh Kareem
An-Najah National University
Bilal Hamamra
An-Najah National University
Sufyan Abuarrah
An-Najah National University

Abstract

This research paper examines ChatGPT's methodologies for addressing the untranslatability challenges posed by verbified proper nouns in English-Arabic translation. The process of denominalization, whereby proper nouns are converted into verbal forms, presents substantial translation difficulties due to its complex intersection of linguistic structure and cultural context. Previous scholarship by Phrisan et al. (2021) has investigated verbified proper nouns specifically in English, demonstrating their unique linguistic transformations and the cultural connotations they carry. Parallel research by Sattar et al. (2023) has explored the phenomenon of verbified common nouns in both Arabic and English, providing valuable insights into the cross-linguistic dimensions of this linguistic process. However, the academic literature currently lacks a focused examination of how verbified proper nouns are handled in translation between Arabic and English, particularly within the realm of artificial intelligence-assisted translation. The present study addresses this research gap by analyzing ChatGPT's approach to this complex translation issue, utilizing Newmark's (1988) theoretical framework on translation strategies. Our findings indicate that ChatGPT employs a user-oriented approach predicated on an assumption of minimal shared cultural knowledge with the end user. This approach leads the AI system to consistently adopt semantic translation strategies, prioritizing the accurate conveyance of meaning and intended effect from the source language to the target language. While this method ensures comprehension and maintains the original text's impact for the target audience, our analysis reveals that its effectiveness remains subject to specific contextual conditions. This investigation makes a significant contribution to understanding AI capabilities in handling linguistically and culturally complex translation tasks. The study highlights both the potential of large language models in overcoming translation challenges and their current limitations when dealing with specialized linguistic phenomena like verbified proper nouns. These findings have important implications for the future development of AI translation systems and their application in cross-cultural communication contexts.

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How to Cite
Kareem , D., Hamamra, B., & Abuarrah, S. (2025). Addressing Untranslatability: ChatGPT’s Compensatory Strategies for Translating Verbified Proper Nouns in English–Arabic Contexts. Traduction Et Langues, 24(01), 298-326. https://doi.org/10.52919/translang.v24i01.1037
Issue
Vol 24 No 01 (2025): Traduction et Langues Volume : 24 Issue : 01/ June 2025
Section
Articles
Author Biographies

Darawsheh Kareem , An-Najah National University

Kareem Darawsheh is an EFL teacher and MA student of Applied Linguistics and Translation in the Department of English Language and Literature at An-Najah National University, Nablus, Palestine.  He has earned an excellence-based exchange scholarship from the University of Ca’ Foscari in Venice, Italy in which he has honed his knowledge and skills in linguistic research. His research interests are in Linguistics, Translation, and Postcolonialism.

Bilal Hamamra, An-Najah National University

Bilal Hamamra has a PhD in Early Modern Drama from the University of Lancaster, UK and currently works as an Assistant Professor of English Literature. His research interests are in Early Modern Drama, Shakespeare, Women’s Writings and Gender Studies. He has published articles on sacrifice, speech, silence and gender in distinguished journals such as Early Modern Literary Studies, Critical Survey, ANQ, Journal for Cultural Research and Changing English,among others.

Sufyan Abuarrah, An-Najah National University

Sufyan Abuarrah received his PhD in Linguistics from Vrije Universitate Brussels (VUB) in 2011. Since then, he has been teaching at the Department of English Language and Linguistics at An-Najah National University, Nablus, Palestine. He is the coordinator of the MA program in Applied Linguistics and Translation. His research interest is particularly interdisciplinary and focuses on language function in relation to language teaching, translation, cultural understanding, and discourse studies.

References

Ahmed, S. A. A. (2022). Technology and artificial intelligence in simultaneous interpreting: A multidisciplinary approach. CDELT Occasional Papers in the Development of English Education, 78(1), 325–353. https://doi.org/10.21608/ opde.2022.249945
Catford, J. C. (1965). A linguistic theory of translation (pp. 93–103). Oxford University Press.
Chow, J. (2019). Lost in translation: Fidelity-focused machine translation evaluation (Doctoral dissertation, Imperial College London).
Clark, E. V., & Clark, H. H. (1979). When nouns surface as verbs. Language, 55(4), 767–811.
Cui, J. (2012). Untranslatability and the method of compensation. Theory and Practice in Language Studies, 2(4), 826–830.
Eszenyi, R., Bednárová-Gibová, K., & Robin, E. (2023, May). Artificial intelligence, machine translation & cyborg translators: A clash of utopian and dystopian visions. Ezikov Svât.
Héois, A. (2020). When proper names become verbs: A semantic perspective. Lexis: Journal in English Lexicology(16). https://doi.org/10.4000/lexis.4788
Imami, T. R., & Mu’in, F. (2021, October). Linguistic and cultural problems in translation. In 2nd International Conference on Education, Language, Literature, and Arts (ICELLA 2021) (pp. 178–186). Atlantis Press.
Jiao, H., Peng, B., Zong, L., Zhang, X., & Li, X. (2024). Gradable ChatGPT translation evaluation. arXiv Preprint. https://arxiv.org/abs/2401.09984
Kadaoui, K., Magdy, S. M., Waheed, A., Khondaker, M. T. I., El-Shangiti, A. O., Nagoudi, E. M. B., & Abdul-Mageed, M. (2023). Tarjamat: Evaluation of Bard and ChatGPT on machine translation of ten Arabic varieties. arXiv Preprint. https://arxiv.org/abs/2308.03051
Khoshafah, F. (2023). ChatGPT for Arabic-English translation: Evaluating the accuracy. Research Square. https://doi.org/10.21203/rs.3.rs-3355981/v1
Mohammadi, B., & Keshavarzi, A. (2016). Cultural translatability and untranslatability: A case study of translation of “Rostam and Sohrab.” Journal of Global Research in Education and Social Science, 6(3), 138–147.
Newmark, P. (1981). Approaches to translation. Pergamon Press.
Newmark, P. (1988). A textbook of translation. Prentice Hall.
Newmark, P. (2000). Approaches to translation. Shanghai Education Press.
Nida, E. A. (2004). Toward a science of translation. Shanghai Foreign Language Education Press.
Peng, K., Ding, L., Zhong, Q., Shen, L., Liu, X., Zhang, M., Ouyang, Y., & Tao, D. (2023, March 24). Towards making the most of ChatGPT for machine translation. arXiv.org. https://arxiv.org/abs/2303.13780
Prasihan, J. N., Widyastuti, W., & Setiawan, S. (2021). The verbification of proper nouns in English. English Language in Focus (ELIF), 4(1), 69–80. https://doi.org/ 10.24853/elif.4.1.69-80
Sankaravelayuthan, R. (2020). Lexical gaps and untranslatability in translation. Language in India, 20(5), 56–82.
Sattar, S., & Abbass, Q. (2023). A contrastive study of denominal verbs in selected English and Arabic religious texts. International Journal of Engineering, Management and Humanities (IJEMH).
Siu, S. C. (2023). ChatGPT and GPT-4 for professional translators: Exploring the potential of large language models in translation. SSRN. https://doi.org/ 10.2139/ ssrn.4448091
Tate, T., Doroudi, S., Ritchie, D., Xu, Y., & Uci, M. W. (2023). Educational research and AI-generated writing: Confronting the coming tsunami. EdArXiv. https://doi.org/10.35542/osf.io/4mec3
Tripathi, S., & Sarkhel, J. K. (2010). Approaches to machine translation. Annals of Library and Information Studies.
Yuxin, C., Wang, R., Chen, J., Chao, Y., Maimaitili, A., & Zhang, H. (2023). Context-based AI translation from a globalization perspective: A case study of ChatGPT. Sino-US English Teaching, 20(9), 370–380.
Zhong, Q., Ding, L., Liu, J., Du, B., & Tao, D. (2023, February 19). Can ChatGPT understand too? A comparative study on ChatGPT and fine-tuned BERT. arXiv Preprint. https://arxiv.org/abs/2302.10781
Zuo, Y., Ching, G. S., & Khotsing, R. (2024, May). The application of ChatGPT in literary translation: A case study from Thai to Chinese. In International Workshop on Learning Technology for Education Challenges (pp. 332–348). Springer Nature. https://doi.org/10.1007/978-3-031-55342-3_26