The Ability of English Education Department Students of University of Mataram to Conduct Post-Editing of Narrative Text for GNMT
DOI:
10.29303/jcar.v5iSpecialIssue.4713Published:
2023-07-18Downloads
Abstract
The emerging of the AI-generated Machine Translation or Neural Machine Translation has continued the role of translator to conduct adequate post-editing for the NMT. This research is aimed to analyze the ability of English Education Department Students of University of Mataram in conducting post-editing of narrative text for Google Translate, one of widely known NMTs. The data was collected through CMC, they are the Interpreting and Translation class students’ worksheet of post-edited narrative text which previously was translated by GT. The data then were analyzed using few theories Baker’s textual equivalence theory, and harmonized metric of quality translation. Through the analyzation result, it could be inferred that most students are able to identify which sentences generated by GNMT that require post-editing and which that do not. Most of sentences left unedited by students are proven well-translated by the GNMT. Regardless, some sentences indeed still need to be post-edited after input to the GNMT, as it seems GNMT still struggles to translate well some phrases or terms that related to the culture of the language. Most students in the Translation and Interpreting class at the University of Mataram have proven their ability to identify and correct errors in GNMT-generated sentences through post-editing. This research is hoped to offer valuable insights for both education and translation and serves as a useful reference for readers seeking comprehension in this field
Keywords:
Google neural machine translation, Narrative text, Post-editing.References
Almira Zulaika, B., Ali Wardana, L., & Farmasari, S. (2022). Students’ Ability to Conduct Pre-Editing of Text Procedure for Google Neural Machine Translation. Journal of Language, 4(2), 173–183.
Baker, M. (1992). In Other Words: A Coursebook on Translation (3rd ed.). Rouletdge.
Bhattacherjee, A. (2012). Social Science Research: Principles, Methods, and Practices. In Social Science Research: Principles, Methods, and Practices (2nd ed., Vol. 2). Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. http://scholarcommons.usf.edu/oa_textbookshttp://scholarcommons.usf.edu/oa_textbooks/3
Cheng, Y. (2019). Joint Training for Neural Machine Translation [Tsinghua University]. http://www.springer.com/series/8790
Cohen, L., Manion, L., & Morisson, K. (2005). Research Methods in Education 5th Edition (5th ed.). RoutLedgeFalmer.
Irfan, M. (2017). Machine Translation. https://www.taus.net/academy/timelines/translation-
Rizki Ramadhan, N., Baharuddin, & Ali Wardana, L. (2021). An Analysis of Translation Method Used in the Novel Earth Translated by Gill Westaway. LISDAYA: Jurnal Linguistik (Terapan), Sastra, Dan Budaya, 17(2), 30–37.
Saputra, A., Sumiati, & Baharuddin. (2022). The Analysis of Google Translate Accuracy in Translating Procedural and Narrative Text. JEEF (Journal of English Education Forum), 2(1), 7–11.
Sari, N., Arifuddin, & Baharuddin. (2022). The Equivalence in The Translation of English Idiomatic Expression into Indonesian by Students of English Education Department University of Mataram. Culturalistics: Journal of Cultural, Literary, and Linguistic Studies, 6(1), 48–58. http://ejournal.undip.ac.id/index.php/culturalistics
The Sage Encyclopedia of Qualitative Research Methods. (2008). In L. M. Given (Ed.), Qualitative Research Methods (Vols. 1–2). Sage Publication Inc.
Tobing, M. (2019). Post-Editing Process of Machine Translation: A Case Study of Google Translate in Local Government Website.
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., Krikun, M., Cao, Y., Gao, Q., Macherey, K., Klingner, J., Shah, A., Johnson, M., Liu, X., Kaiser, Å., Gouws, S., Kato, Y., Kudo, T., Kazawa, H., … Dean, J. (2016). Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. http://arxiv.org/abs/1609.08144
Yani, S., Ezir, E., Daulay, I. K., & Manugeren, M. (2022). Teaching Narrative Text Through MindMapping Technique. Journal of Language, 4(1), 73–82.
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Copyright (c) 2023 Rani Dwi Hapsari, Baharuddin, Lalu Ali Wardana, Santi Farmasari
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