A comparative analysis of Google Translate and ChatGPT in translating Borneo Bulletin News into standard Malay
DOI:
https://doi.org/10.33474/j-reall.v6i1.23252Keywords:
ChatGPT-4, Google Translate, machine translation, news translation, standard MalayAbstract
In the digital era, machine translation tools like Google Translate and ChatGPT have become essential for cross-linguistic communication, particularly in translating formal texts such as news articles. This study aims to evaluate the effectiveness of these tools in translating English news articles from Borneo Bulletin into Standard Malay, focusing on lexical accuracy, syntactic structure, and contextual appropriateness. A qualitative comparative analysis was conducted by translating selected articles using both Google Translate and ChatGPT-4. The outputs were manually analyzed using a qualitative rubric guided by meaning-based translation theory, focusing on word choice, sentence structure, the original tone, and cultural nuances. The results indicate that ChatGPT-4 outperforms Google Translate in maintaining lexical precision, grammatical coherence, and contextual relevance. ChatGPT-4 demonstrated superior handling of idiomatic expressions and complex sentence constructions, producing translations that adhered closely to journalistic norms in Standard Malay. However, both tools exhibited limitations in accurately translating culturally specific references and specialized terminology. Google Translate, despite improvements through neural machine translation, tended to produce overly literal translations, leading to a loss of tone and clarity in formal contexts. These findings highlight the potential and limitations of machine translation in journalism, where accuracy and tone are crucial for public trust and information dissemination. The study emphasizes the necessity for enhanced machine translation algorithms and the integration of human feedback to elevate translation quality in low-resource languages such as Malay.
References
Adnan, T. H., Apandi, M. M., Kamaruddin, H., Salowi, M. A., Law, K. B., Haniff, J., & Goh, P. P. (2018). Catquest-9SF questionnaire: Validation of Malay and Chinese-language versions using Rasch analysis. Health and Quality of Life Outcomes, 16(1). https://doi.org/10.1186/s12955-017-0833-3
AlAqad, M. H., & Al-Saggaf, M. A. (2021). Issues in translating cultural terms between English and Malay: A comparative analysis. Pedagogical Research, 6(4). https://doi.org/10.29333/pr/11272
Alkhawaja, L. (2024). Unveiling the new frontier: ChatGPT-3 powered translation for Arabic-English language pairs. Theory and Practice in Language Studies, 14(2), 347-357. https://doi.org/10.17507/tpls.1402.05
Alkhawaja, L., Ibrahim, H., Ghnaim, F., & Awwad, S. (2020). Neural machine translation: Fine-grained evaluation of Google Translate output for English-to-Arabic translation. International Journal of English Linguistics, 10(4), 43. https://doi.org/10.5539/ijel.v10n4p43
Alosaimi, B. A., & Alawad, N. A. (2024). Evaluation of the translation of separable phrasal verbs generated by ChatGPT. Open Science Framework. https://doi.org/10.31235/osf.io/y6mca
Broussard, M., Diakopoulos, N., Guzman, A. L., Abebe, R., Dupagne, M., & Chuan, C. H. (2019). Artificial intelligence and journalism. Journalism & mass communication quarterly, 96(3), 673-695. https://doi.org/10.1177/1077699019859901
Chang, P. L., & Hsu, C. H. (2022). The feasibility of Chinese–English machine translation applied to academic texts: Using thesis abstracts from National Digital Library of Theses and Dissertations (NDLTD) in Taiwan. Translogos Translation Studies Journal, 5(2), 60–83. https://doi.org/10.29228/translogos.47
Feng, Y., Hu, Q., & Yang, S. (2024). Improving machine translation and post-editing for Chinese tourism texts using transformer-based models. Applied and Computational Engineering, 34(1), 7-12. https://doi.org/10.54254/2755-2721/34/20230286
Jiang, F., & Hyland, K. (2024). Does ChatGPT argue like students? Bundles in argumentative essays. Applied Linguistics, amae052. https://doi.org/10.1093/applin/amae052
Jiao, W., Wang, W., Huang, J. T., Wang, X., & Tu, Z. (2023). Is ChatGPT a good translator? Yes, with GPT-4 as the engine. arXiv Preprint. https://doi.org/10.48550/arxiv.2301.08745
Karabayeva, I., & Kalizhanova, A. (2024). Evaluating machine translation of literature through rhetorical analysis. Journal of Translation and Language Studies, 5(1), 1-9. https://doi.org/10.48185/jtls.v5i1.962
Lai, V. D., Ngo Trung, N., & Veyseh, A. (2023). ChatGPT beyond English: Towards a comprehensive evaluation of large language models in multilingual learning. Findings of the Association for Computational Linguistics: EMNLP 2023. https://doi.org/10.18653/v1/2023.findings-emnlp.878
Larson, M. L. (1984). Meaning-based translation: A guide to cross-language equivalence. Lanham, MD: University Press of America.
Li, N. (2024). Analysis of the global literary significance of foreign novel translations in late Qing China: A case study of ‘Novels Translated by Lin Shu’ (1899–1919). Critical Survey, 36(3), 29-47. https://doi.org/10.3167/cs.2024.360304
Naveen, P., & Trojovský, P. (2024). Overview and challenges of machine translation for contextually appropriate translations. Iscience, 27(10).
Salleh, P. A. H. B. M., Ton, D. M. R., Sabdin, M. M., & Kadhim, A. D. K. A. P. (2024). Bridging the resource gap for Malay-to-Arabic translation: Evaluating machine translation of news headlines. International Journal of Academic Research in Progressive Education and Development, 13(3). https://doi.org/10.6007/ijarped/v13-i3/22148
Santy, S., Dandapat, S., Choudhury, M., & Bali, K. (2019). INMT: Interactive neural machine translation prediction. https://doi.org/10.18653/v1/d19-3018
Şahin, M., & Gürses, S. (2021). English-Turkish literary translation through human-machine interaction. Tradumàtica Tecnologies De La Traducció, 19, 171-203. https://doi.org/10.5565/rev/tradumatica.284
Siregar, S. D. P., Al Farisi, M. Z., & Naser, A. D. M. (2024). Mapping the landscape of machine translation error research: A bibliometric review (1980-2023). Buletin Al-Turas, 30(1). https://doi.org/10.15408/bat.v30i1.32655
Soysal, F. (2023). Enhancing translation studies with Artificial Intelligence (AI): Challenges, opportunities, and proposals. Karamanoğlu Mehmetbey Üniversitesi Uluslararası Filoloji ve Çeviribilim Dergisi, 5(2), 177-191. https://doi.org/10.55036/ufced.1402649
Wang, J. (2024). Bibliometric insights into translation technology: A CiteSpace analysis of Web of Science core collection publications (2000-2024). Translating the Future: Exploring the Impact of Technology and AI on Modern Translation Studies, 85-136. https://doi.org/10.46679/9788196780593ch05
Yaqin, L. N., Yusof, B., Yusof, N., & Damit, A. R. (2025). Students' perception of using ChatGPT as an AI-integrated tool in the Malay Language. Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika, 9(1), 13-31. https://doi.org/10.36312/e-saintika.v9i1.2584
Yaqin, L. N. (2025). Emerging research in AI-assisted language learning: A systematic literature review. Multi-Industry Digitalization and Technological Governance in the AI Era, 29-50. https://doi.org/10.4018/979-8-3373-1681-9.ch002
Zaki, M. Z., & Ahmed, U. (2024). Bridging linguistic divides: The impact of AI-powered translation systems on communication equity and inclusion. Journal of translation and language studies, 5(2), 20-30. https://doi.org/10.48185/jtls.v5i2.1065
Zayyanu, Z. M. (2024). Transforming worlds: The intersection of translation technology and transformers in the digital age. Computer Science & Engineering: An International Journal. 14(3), 1-14. https://doi.org/10.5121/cseij.2024.14301
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