The Translation Problem of Equivalence on Harry Potter and the Order of the Poenix
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Abstract
The findings of this study demonstrate that achieving true equivalence in machine translation, particularly when dealing with literary texts like Harry Potter and the Order of the Phoenix, presents a range of complex challenges. Through a detailed comparison based on Mona Baker’s theory, the analysis identifies frequent issues at the lexical, grammatical, textual, and pragmatic levels. Google Translate, despite its efficiency in generating rapid translations, often fails to accurately render expressions with cultural significance, nuanced emotional tones, and figurative language. Idiomatic phrases, in particular, tend to be translated literally, stripping them of their intended meanings and stylistic impact. Grammatical inconsistencies are also observed, such as incorrect tense usage, awkward word order, and the omission of important syntactic elements, all of which compromise the clarity and naturalness of the target text.Textual cohesion and pragmatic appropriateness are similarly affected. Translated segments sometimes lack logical flow or contextual relevance, which hinders readers’ comprehension and disrupts the immersive experience that is essential to literary storytelling. The absence of cultural sensitivity in translation is especially evident in references unique to the source culture, which are either misinterpreted or rendered in ways that do not resonate with the Indonesian audience. These recurring challenges highlight the limitations of relying solely on machine translation tools for literary works, where meaning is layered and context-dependent. Although machine translation can serve as a useful preliminary tool, its outputs require extensive human intervention to ensure both linguistic accuracy and cultural fidelity. The study ultimately emphasizes the need for integrating post-editing practices into translator training programs, not only to improve translation quality but also to develop students’ analytical skills and intercultural competence in handling complex texts in the digital age.
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Agulló Garcia, B., et al. (2024). Indirect (pivot) audiovisual translation: Perspectives, 32(5), 849.
Aleksandrova, E. V., Rubtsova, S. Y., Timofeeva, L. L., Morozova, M. N., Dudkina, A. I., & Tyrkheeva, N. S. (2024). Investigating the hermeneutical equivalence of idioms in translation. Cadernos de Educação Tecnologia e Sociedade, 17(2), 781–788.
Almaaytah, S. A. (2022). Post‑editing in translation: Experiences and development. Journal of Positive School Psychology, 6(4), 8794–8803.
Arenas, A. G., & Toral, A. (2021). The impact of post‑editing and machine translation on creativity and reading experience. Cornell University.
Briva‑Iglesias, V. (2021). Traducción humana vs traducción automática: análisis contrastivo de procesos de equivalencia. Mutatis Mutandis, 14(2), 571.
Castilho, S., & Resende, N. (2022). Post‑editese in literary translations. Information, 13(2), 66.
Corpas Pastor, G., & Noriega‑Santiáñez, L. (2024). Human versus neural machine translation creativity: A study on manipulated MWEs in literature. Information, 15(9), 530.
Deng, X., & Yu, Z. (2022). A systematic review of MT‑assisted language learning for sustainable education. Sustainability, 14(13), 7598.
Dewayanti, D. P. S. P., & Margana, M. (2023). The impact of contextual understanding on neural machine translation accuracy: A case study of Indonesian cultural idioms in English translation. Englisia: Journal of Language, Education, and Humanities, 2023.
Guerberof Arenas, A. (2022). Post‑editing and translator creativity: Effects on reading experience and translation quality. Translation Spaces, 11(2), 185–202.
Guerberof‑Arenas, A., & Moorkens, J. (2023). Ethics and machine translation: The end‑user perspective. In Towards Responsible Machine Translation (Machine Translation: Technologies and Applications, Vol. 4), 113.
Guerreiro, N. M., Alves, D., Waldendorf, J., Haddow, B., Birch, A., Colombo, P., & Martins, A. F. T. (2023). Hallucinations in large multilingual translation models. Transactions of the Association for Computational Linguistics, 11, 1500–1517.
He, L., Ghassemiazghandi, M., & Subramaniam, I. (2024). Comparative assessment of Bing Translator and Youdao Machine Translation Systems in English-to-Chinese literary text translation. Forum for Linguistic Studies, 6(2), Article 1189.
Hulley, B. (2024). Assessing human translation style with the help of NMT: A case study of French language comic book translators. Palimpsestes, 38.
Hussinoor, Y., Hu, K., & Pym, A. (2024). Who’s afraid of literary post‑editing? New Frontiers in Translation Studies, 263.
Li, T. (2023). Exploring failures and possible remedies in AI and human translation of English idioms. Transactions on Social Science, Education and Humanities Research. https://doi.org/10.62051/199nqb23
Liu, E., Chaudhary, A., & Neubig, G. (2023). Crossing the threshold: Idiomatic machine translation through retrieval augmentation and loss weighting.
Marhamah, H., Hidayati, D., & Prasatyo, B. A. (2024). Equivalence challenges in machine translation: An analysis of Google Translate output through Mona Baker’s theory (2011) and post‑editing strategies. International Journal of Economics, Management, Business, and Social Science (IJEMBIS), 4.
Nilam Saria, A., Arifuddin, & Baharuddin. (2022). The equivalence in the translation of English idiomatic expression into Indonesian by students. Culturalistics, 6, 48–58.
Oneţ, A.‑E., et al. (2023). Equivalence in translation: Translation studies as an interdiscipline. Land Forces Academy Review, 28(1), 39–44.
Pudjiati, D., Lustyantie, N., Iskandar, I., & Fitria, T. N. (2023). Post‑editing of machine translation: better translation of culturally specific terms. Language Circle: Journal of Language and Literature.
Sacramento, N. M., & Pascoal, J. (2024). The link between translation difficulty and machine translation quality: A review and empirical investigation. Language Resources and Evaluation, 58, 1093–1114.
Sarti, G., Bisazza, A., Arenas, A. G., & Toral, A. (2022). DivEMT: Neural machine translation post‑editing effort across typologically diverse languages.
Sibuea, T., Budi, W., & Jonathan, K. (2023). The equivalence problems produced by machine translation on a literary text: A study on the Indonesian translation of Harry Potter and the Order of the Phoenix. Culturalistics, 7.
Sinambela, E., Carolina, & Situmorang, A. (2022). Equivalence translation: student mastery in translating Indonesian idiomatic expressions to English. Jurnal Ilmu Sosial Mamangan.
Su, W., & Li, D. (2023). The effectiveness of translation technology training: A mixed‑methods study. Humanities and Social Sciences Communications, 10.
Susiyati, S. (2022). Meaning equivalence in the translation of idioms from English to Indonesian using “Google Translate”. JOEL: Journal of Educational and Language Research, 2(1), 179–186.
Terribile, S. (2023). Terribile, S. (2023, December 19). Is post editing really faster than human translation? Translation Spaces: a multidisciplinary, multimedia, and multilingual journal of translation.
Waldendorf, J., Haddow, B., & Birch, A. (2024). Contrastive decoding reduces hallucinations in large multilingual machine translation models. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (pp. 2526–2539). Association for Computational Linguistics.
Yang, Y., Liu, R., Qian, X., & Ni, J. (2023). Performance and perception: MT post‑editing in Chinese‑English news translation by novice translators. Humanities and Social Sciences Communications, 10.