A Comparative Analysis of Translation Techniques between YouTube Subtitle Translation and the DeepL Translation Tool in the Song Lyrics of Shake It Off by Taylor Swift

Agneso Feronika Limbong; Haidah Situmorang; Juliva Viona Banjarnahor; Aryadi Manuel Gultom

doi:10.69820/jeltlal.v3i2.401

Authors

Agneso Feronika Limbong Universitas HKBP Nommensen
Haidah Situmorang Universitas HKBP Nommensen
Juliva Viona Banjarnahor Universitas HKBP Nommensen
Aryadi Manuel Gultom Universitas Negeri Yogyakarta

DOI:

https://doi.org/10.69820/jeltlal.v3i2.401

Keywords:

Translation Technique, Subtitle, DeepL, Lyrics translation, Machine translation

Abstract

This study uses a descriptive qualitative approach to examine the translation techniques used in the translated version of the song "Shake It Off" by Taylor Swift into Indonesian. The analysis compares two translations, namely the subtitle from YouTube (TT1) and the machine translation using the platform DeepL (TT2). This study refers to the framework of translation techniques developed by Molina and Albir (2002), as well as the interactive data analysis model of Miles and Huberman (1994) which includes the process of data condensation, data presentation, and conclusion drawing. The song lyrics were divided into units of analysis and arranged parallel to their respective translated versions to identify and classify the techniques used. The findings show that there is a significant difference in the application of translation strategies between TT1 and TT2. TT2 relies heavily on literal translation techniques (68 cases or 39.5%), reflecting the tendency to maintain the surface structure of the source text. In contrast, TT1 shows a higher diversity of techniques, especially adaptation (28 cases or 16.2%) and reduction (11 cases or 6.3%) techniques, reflecting the translator's sensitivity to the cultural context and audiovisual limitations. Other techniques such as amplification, borrowing and modulation also show variations in frequency of use between the two versions. In general, this study shows that human and machine interpreters have different priorities in conveying meaning, style and nuance in the context of translating song lyrics. These differences provide important insights into the advantages and limitations of each approach in the field of audiovisual translation.

References

Aini, N., Alifahul, E., Wulan, S., Muhyiddin, M., Gian, G., & Herdina, H. (2023). A analysis on translation procedures used in the abstract translation of education journal. English National Seminar, 1–10.

Birdsell, B. (2022). Student Writings with DeepL: Teacher Evaluations and Implications for Teaching. JALT Postconference Publication, 2021(1), 117. https://doi.org/10.37546/jaltpcp2021-14

Bowker, L. (n.d.). De- mystifying Translation. Routledge.

Chien, P. S. C., & Hui-chin Lin, G. (2009). Machine Translation for Academic Purposes. Proceedings of the International Conference on TESOL and Translation, December, 133–148.

Flettrich, L., & Hardin, H. (2023). Taylor Swift, Pop Music, and the Creation of Modern Folklore. 11(1).

Freskila, E. P., Agung, I. G., & Rwa, S. (2025). Translation Equivalence of Tourism Website Content : A Comparison Between Google Translate and DeepL Translate. 10(May), 1–20.

Galle, M. (2021). Unterrichtszentrierte Schulentwicklung. In Unterrichtszentrierte Schulentwicklung. https://doi.org/10.1007/978-3-658-35070-3

Gao, R., Lin, Y., Zhao, N., & Cai, Z. G. (2024). Machine translation of Chinese classical poetry: a comparison among ChatGPT, Google Translate, and DeepL Translator. Humanities and Social Sciences Communications, 11(1), 1–10. https://doi.org/10.1057/s41599-024-03363-0

Gritsenko, E., & Aleshinskaya, E. (2016). Translation of Song Lyrics as Structure-related Expressive Device. Procedia - Social and Behavioral Sciences, 231(May), 165–172. https://doi.org/10.1016/j.sbspro.2016.09.087

Molina, L., & Albir, A. H. (2002). Translation techniques revisited: A dynamic and functionalist approach. Meta, 47(4), 498–512. https://doi.org/10.7202/008033ar

Palazzolo, D. J. (2023). Research Methods. Experiencing Citizenship: Concepts and Models for Service-Learning in Political Science, 109–118. https://doi.org/10.4324/9781003444718-9

Perego, E. (2015). Eye-tracking in audiovisual translation A . January 2012. https://doi.org/10.13140/2.1.4680.5446

Polakova, P., & Klimova, B. (2023). Using DeepL translator in learning English as an applied foreign language – An empirical pilot study. Heliyon, 9(8), e18595. https://doi.org/10.1016/j.heliyon.2023.e18595

Polcz, K. (2008). Jorge Díaz Cintas and Aline Remael: Audiovisual translation: Subtitling. Across Languages and Cultures, 9(2), 291–299. https://doi.org/10.1556/acr.9.2008.2.8

Poluwa, M. I., & Nafilaturif’ah, N. (2021). Audiovisual and Song Translation of Indonesian Subtitle in Shelter Music Video. Language Literacy: Journal of Linguistics, Literature, and Language Teaching, 5(2), 332–339. https://doi.org/10.30743/ll.v5i2.4481

Putri, S. A., & Dewi, H. D. (2021). the Translation of Entertainment News From English To Indonesian With Machine Translation. Journal of English Language and Culture, 11(2), 153–168. https://doi.org/10.30813/jelc.v11i2.2637

Suratno, A., & Wijaya, D. C. (2018). Text Reduction: Strategies Adopted in Audio Visual Subtitle Translation. 228(Klua), 205–213. https://doi.org/10.2991/klua-18.2018.30

Yang, J.-S. (2014). Aesthetic Object and Subject in Song Translation. English Language and Literature Studies, 4(4), 25–30. https://doi.org/10.5539/ells.v4n4p25