Decoding Figurative Language in Journalism Translation: Human and LLM Translation of English-Vietnamese Metaphors
DOI:
https://doi.org/10.17507/tpls.1610.22Keywords:
large language models, metaphor translation, journalistic discourse, English–Vietnamese translation, cultural equivalenceAbstract
The increasing use of large language models (LLMs) in professional translation has reshaped multilingual news production. While prior research has reported gains in fluency and surface-level accuracy, limited attention has been paid to LLMs’ ability to translate figurative language, which plays a key role in meaning construction and evaluative stance in journalistic discourse. This study investigates the extent to which state-of-the-art LLMs achieve functional and cultural equivalence in the English–Vietnamese translation of journalistic metaphors, in comparison with professional human translators. Adopting a mixed-methods comparative design, the study analyzes a purposively selected corpus of 50 metaphorical sentences from major international news outlets. Translation outputs produced by two LLMs (GPT-4o and Claude 3.5) under two prompting conditions (zero-shot and advanced prompting) were compared with a human benchmark generated by senior Vietnamese news editors. All translations were evaluated through blind expert assessment based on four criteria: semantic accuracy, naturalness, cultural equivalence, and journalistic style. The findings show that advanced prompting significantly improves AI performance, particularly in semantic accuracy and stylistic fluency. However, LLMs continue to underperform in cultural equivalence, often exhibiting literalism and reduced metaphorical resonance. These findings underscore the indispensable role of human expertise as cultural and pragmatic mediators, suggesting that while LLMs accelerate production, human transcreation remains essential to preserving the rhetorical integrity of multilingual journalism.
References
Castilho, S., Doherty, S., Gaspari, F., & Moorkens, J. (2022). Machine translation quality assurance: From logic to linguistics. Routledge.
Charteris-Black, J. (2011). Politicians and rhetoric: The persuasive power of metaphor (2nd ed.). Palgrave Macmillan. https://doi.org/10.1057/9780230319141
Hendy, A., Abdelrehim, M., Sharaf, A., Raunak, V., Gabr, H., Labaka, G., & Awadalla, H. (2023). How good are GPT models at machine translation? A comprehensive evaluation. arXiv. https://arxiv.org/abs/2302.09210
House, J. (2015). Translation quality assessment: Past and present (2nd ed.). Routledge. https://doi.org/10.4324/9781315679662
Kövecses, Z. (2010). Metaphor: A practical introduction (2nd ed.). Oxford University Press.
Kocmi, T., Elekes, R., Federmann, C., & Germann, U. (2023). WMT23 metrics shared task results: LLMs are robust, but reference-based metrics are still needed. In Proceedings of the Eighth Conference on Machine Translation (WMT 2023) (pp. 113–132). Association for Computational Linguistics.
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2023). Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, 35, 22199–22213.
Lakoff, G., & Johnson, M. (1980). Metaphors we live by. University of Chicago Press.
Littlemore, J., & Low, G. (2006). Figurative thinking and foreign language learning. Palgrave Macmillan.
Lucas, M. (2023). AI in journalism: The evolution of news production and translation. Oxford University Press.
Moorkens, J., & O’Brien, S. (2023). Translation and technology. Routledge.
Nida, E. A. (1964). Toward a science of translating. E. J. Brill.
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.
Saldanha, G., & O’Brien, S. (2014). Research methodologies in translation studies. Routledge.
Schäffner, C. (2004). Political discourse analysis from the point of view of translation studies. Journal of Language and Politics, 3(1), 117–150. https://doi.org/10.1075/jlp.3.1.09sch
Schäffner, C. (2017). Metaphor in translation. In K. Malmkjær (Ed.), The Routledge handbook of translation studies and linguistics (pp. 247–262). Routledge.
Steen, G. J., Dorst, A. G., Herrmann, J. B., Kaal, A., Krennmayr, T., & Pasma, T. (2010). A method for linguistic metaphor identification: From MIP to MIPVU. John Benjamins.
Toury, G. (2012). Descriptive translation studies—and beyond (Revised ed.). John Benjamins.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837.
