Human vs. ChatGPT’s translation strategies: A comparative study of religious terms in children’s literature from English into Arabic
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Abstract
Largely because of the emergence of neural machine translation, which has produced notable gains in translation quality, the field of machine translation has advanced quickly in recent years. At the same time, artificial intelligence has shown amazing advancements; its uses now include translation assistance, which draws great attention in many different sectors. As culture is one of the main problems faces the translators, religious terms and expression need to be carefully translated as they are very critical and sensitive. This paper compares the performance of GPT-3 with that of human translators by examining the techniques used by GPT-3 in translating religious and culturally specific items. Religious terms are taken from three children’s stories addressing particular cultural concerns for this purpose. The translated religious terms and expressions extracted from the Arabic version for both professional human translator and GPT-3 have been analyzed and examined. The study categorizes the chosen items using established theoretical frameworks based on translation strategies: direct translation, retention, specification, generalization, substitution, and omission. The translation performance of GPT-3 is compared with that of the human translator using a descriptive analysis technique. The results indicate that GPT-3 appears to rely on a limited number of strategies, whereas the human translator employs a greater variety of techniques.
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References
Biswas, S. (2023). Role of ChatGPt in education. Journal of ENT Surgery Research, 1, 01–03. Retrieved from: https:// ssrn.com/abstract=4369981
Cady, l., tsou, B. K., & lee, J. S. (2023). Comparing Chinese-english Mt performance involving ChatGPt and Mt pro viders and the efficacy of ai mediated post-editing. Proceedings of Machine Translation Summit XIX, 2, 205–216. Retrieved from: https://aclanthology.org/2023.mtsummit-users.20
Dam, h. V., & Zethsen, K. K. (2016). “i think it is a wonderful job”: on the solidity of the translation profession. Journal of Specialised Translation, (25), 174–187.
Deng, J., & Lin, Y. (2023). The benefits and challenges of chatgpt: An overview. Frontiers in Computing and Intelligent Systems, 2(2), 81–83. https://doi.org/10.54097/fcis.v2i2.4465
Espindola, E., & Vasconcellos, M. L. (2004). Cultural Representation and Subtitling Practices: Cidade de Deus and Boyz ‘N the Hood. In ANAIS: CD-ROM: III – CIATI – Congresso Ibero-Americano de Tradução e Interpretação: Novos tempos, Velha arte. essay.
Felten, e., Raj, M., & Seamans, R. (2023). how will language modelers like ChatGPt affect occupations and industries?. arXiv preprint arXiv:2303.01157. Retrieved from: https://doi.org/10.488550/arXiv2303.01157
Firat, M. (2023). January) how ChatGPt can transform autodidactic experiences and open education? OFS Preprint, https://doi.org/10.31219/osf.io/9ge8m
Gao, J., Zhao, h., Yu, C., & Xu, R. (2023). exploring the feasibility of ChatGPt for event extraction. arXiv preprint arX iv:2303.03836. Retrieved from: https://doi.org/10.48550/arXiv.2303.03836
Gao, Y., Wang, R., & Hou, F. (2023). Unleashing the power of ChatGPT for translation: An empirical study. arXiv Preprint.
Ghosh, S., & Caliskan, a. (2023 ChatGPT perpetuates gender bias in machine translation and ignores non-gendered pro nouns: Findings across bengali and five other low-resource languages [Paper presentation]. in Proceedings of the 2023 aaai/aCM Conference on ai, ethics, and Society, (pp. 901–912). https://doi.org/10.1145/3600211.3604672
Godwin-Jones, R. (2022). Partnering with ai: intelligent writing assistance and instructed language learning. Language Learning & Technology, 26(2), 5–24. https://hdl.handle.net/10125/73474
Hendy, A., Abdelrehim, M., Sharaf, A., Raunak, V., Gabr, M., Matsushita, H., Kim, Y. J., Afify, M., & Awadalla, H. H. (2023, February 18). How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation. arXiv.org. https://arxiv.org/abs/2302.09210
İpek, Z. h., Gözüm, a. i. C., Papadakis, S., & Kallogiannakis, M. (2023). educational applications of the ChatGPt ai system: a systematic review research. Educational Process International Journal, 12(3), 26–55. https://doi. org/10.22521/edupij.2023.123.2
Jiao, W., Wang, W., huang, J., Wang, X., & tu, Z. (2023). January 3). is ChatGPt a good translator? a preliminary study. Preprint. Retrieved from https://wxjiao.github.io/downloads/tech_ chatgpt_arxiv.pdf
Jun, X. (2019). Dialogues on the Theory and Practice of Literary Translation. Routledge. Retrieved from: https://doi. org/10.4324/9780429287848
Kalla, d., & Smith, n. (2023). Study and analysis of ChatGPt and its impact on different fields of study. International Journal of Innovative Science and Research Technology, 8, 827–833. https://doi.org/10.5281/zenodo.7767675
Kannan, J., & Munday, P. (2018). New trends in second language learning and teaching through the lens of ICT, networked learning, and artificial intelligence.
Kenny, d. (2022). Machine translation for everyone: Empowering users in the age of artificial intelligence. language Science Press. Retrieved from: https://library.oapen.org/handle/20.500.12657/61713
Khoshafah, F. (2023). ChatGPt for arabic-english translation: evaluating the accuracy. Research Square, 1–19. Retrieved from: https://doi.org/10.21203/rs.3.rs-2814154/v2
Klingberg, G. (1986). Children's Fiction in the Hands of the Translators. Gleerup.
larroyed, a. (2023). Redefining Patent translation: the influence of ChatGPt and the urgency to align patent lan guage regimes in europe with progress in translation technology. GRUR International, 72(11), 1009–1017. https:// doi.org/10.1093/grurint/ikad099
lee, S., lee, J., Moon, h., Park, C., Seo, J., eo, S., Koo, S., & lim, h. (2023). a survey on evaluation metrics for machine translation. Mathematics, 11(4), 1006. https://doi.org/10.3390/math11041006
Munday, J. (2004). Introducing Translation Studies. Routledge. Retreived from: https://doi.org/10.4324/9780429352461
Newmark, P. (2010). A Textbook of Translation. Shang hai wai yu jiao yu chu ban she. Pedersen J. (2005). How is culture rendered in subtitles? In Gerzymisch H., Nauert S. (Eds.), Challenges of multidimensional translation: Conference proceedings (pp. 1–18). Paper presented at EU High Level Scientific Conference Series, Saarbrücken, Germany. MuTra.
Peng, K., ding, l., Zhong, Q., Shen, l., liu, X., Zhang, M., … tao, d. (2023). towards making the most of chatgpt for machine translation. arXiv preprint arXiv:2303.13780 https://doi.org/10.48550/arXiv.2303.13780
Pedersen, J. (2005, May). How is culture rendered in subtitles. In MuTra 2005–Challenges of Multidimensional Translation: Conference Proceedings (pp. 1-18).
Rankawat, d. (2017). Challenges of translating literature. Sonnets–dr. Mehzbeen Sadriwala. 30, 13, 118–125.
Rendall, S., & Benjamin, W. (1997). the translator’s task. TTR: traduction, terminologie, rédaction, 10, 151–165. https:// doi.org/10.7202/037302
Sanz-Valdivieso, l., & lópez-arroyo, B. (2023). Google translate vs. ChatGPT: Can non-language professionals trust them for specialized translation? [Paper presentation]. in international Conference human-informed translation and interpreting technology (hit-it 2023,) (pp. 97–107). https://doi.org/10.26615/issn.2683-0078.2023_008
Sayaheen, M., Sayaheen, B., & Malkawi, M. (2024). Pre-Translating Process in Literary Text. World Journal of English Language, 14(3).
Siu, S. C. (2023). ChatGPt and GPt-4 for professional translators: exploring the potential of large language models in translation. Available at https://doi.org/10.2139/ssrn.4448091
Van Bulck, l., & Moons, P. (2024). What if your patient switches from dr. Google to dr. ChatGPt? a vignette-based survey of the trustworthiness, value, and danger of ChatGPt-generated responses to health questions. European Journal of Cardiovascular Nursing, 23(1), 95–98. https://doi.org/10.1093/eurjcn/zvad038
Yamada, M. (2009). a study of the translation process through translators’ interim products. Interpreting and Translation Studies: The Journal of the Japan Association for Interpreting and Translation Studies, 9, 159–176. https://doi. org/10.50837/its.0910
Yousef, t. (2012). literary translation: old and new challenges. International Journal of Arabic-English Studies, 13(1), 49–64. https://doi.org/10.33806/ijaes2000.13.1.4