The Translation of Multilingual Signboards in Mataram City Using Google NMT
DOI:
10.29303/jipp.v10i4.4100Published:
2025-11-20Downloads
Abstract
This research investigates the use of Google Neural Matching Translation (GNMT) in translating multilingual signboards in Mataram City into multiple languages. In this paper, the accuracy, readability, and quality of GNMT translations are evaluated to observe their appropriateness in selected well-turned communication environments. Following previous studies, the current study will investigate accuracy, readability, and quality of GNMT translators outputs on translating multilingual signboards in Mataram city compared to its sets produced by human translators. Field work was carried out between June 9 and July 4, 2025 on Jl. Majapahit, Mataram City, West Nusa Tenggara. The population was all multilingual public signboards in Mataram City, while the sample was those along Jl. Majapahit in Mataram. A total of 209 signs (both monolingual and multilingual) were purposively sampled for accuracy, readability and the quality. The results of the study revealed that GNMT achieved a mean score of 4.37 in terms of accuracy and intelligibility as compared to human’s score 5.0. It is good in translating transparent structured texts but does not well with translation cultural idiomatic expressions, where human post editing for adding context make it consistent and informative. On the whole, GNMT delivers 100% OCR success in multilingual signboard translation; it overcomes on clear language but fails to deal with idiomatic expressions and culturally sensitive ones such as name of foods which are indicative that humans are the most suitable part to handle context dependent or culture specific issues.
Keywords:
GNMT, Multilingual, Signboards, Translation Accuracy, Translation ReadabilityReferences
Alhaisoni, E., & Alhaysony, M. (2017). An investigation of Saudi EFL university students’ attitudes towards the use of Google Translate. International Journal of English Language Education, 5(1), 72. https://doi.org/10.5296/ijele.v5i1.10696
Badan Pusat Statistik Provinsi Nusa Tenggara. (2025). Perkembangan Pariwisata Nusa Tenggara Barat Desember 2024. https://ntb.bps.go.id/en/pressrelease/2025/02/03/1033/perkembangan-pariwisata-nusa-tenggara-barat-desember-2024.html?utm
Baharuddin, B., Amin, M., Thohir, L., & Wardana, L. A. (2022). Penerapan teori terjemahan pada editing hasil terjemahan Google Translate pada teks akademik oleh mahasiswa Universitas Mataram. Jurnal Ilmiah Profesi Pendidikan, 6(4), 816–824. https://doi.org/10.29303/jipp.v6i4.390
Baharuddin, B., Putera, L. J., Wardana, L. A., Farmasari, S., & Sukri, M. (2024). Bilingual multilingual signboards on Lombok: Approaches to acquiring the translation equivalence. World Journal of English Language, 14(5), 612–626. https://doi.org/10.5430/wjel.v14n5p612
Baker, M. (2018). In other words: A coursebook on translation. Routledge.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Castilho, S., Moorkens, J., Gaspari, F., & Others. (2018). Assessing quality in machine translation: A case for human evaluation.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th, Ed.). Sage Publications.
Gaspari, F., Almaghout, H., & Doherty, S. (2020). A comparative study of human and machine translation in multilingual communication.
Koehn, P., & Knowles, R. (2017). Six challenges for neural machine translation. ArXiv Preprint ArXiv:1706.03872. https://arxiv.org/abs/1706.03872
Nababan, M. R., Nuraeni, A., & Sumardiono. (2012). Pengembangan model penilaian kualitas terjemahan. Kajian Linguistik Dan Sastra, 24(1), 39–57. https://doi.org/10.23917/kls.v24i1.133
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544. https://doi.org/10.1007/s10488-013-0528-y
Saputra, A. (2022). The analysis of Google Translate accuracy in translating procedural and narrative text. Journal of English Education Forum (JEEF), 2, 7–11.
Wardana, L. A., Baharuddin, B., & Nurtaat, L. (2022). Kemampuan mahasiswa melakukan post-editing terhadap hasil terjemahan machine translation. Jurnal Ilmiah Profesi Pendidikan, 7(1), 53–61. https://doi.org/10.29303/jipp.v7i1.392
Wu, Y., Schuster, M., Chen, Z., Le, Q. V, Norouzi, M., Macherey, W., & Dean, J. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. ArXiv Preprint ArXiv:1609.08144. https://arxiv.org/abs/1609.08144
Zhou, L., Li, H., & Zhao, T. (2020). OCR accuracy and its impact on multilingual machine translation.
Zhu, Y., Lal, D. M., Denysiuk, S., & Mitkov, R. (2024). From neural machine translation to large language models: Analysing translation quality of Chinese idioms. Proceedings of the International Conference on New Trends in Translation and Technology Conference 2024, 247–260. https://doi.org/10.26615/issn.2815-4711.2024_021
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Copyright (c) 2025 Nurshahifah Fithri, Baharuddin Baharuddin, Lalu Ali Wardana
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