Analysis of Two Translation Applications : Why is DeepL Translate more accurate than Google Translate?

Authors

  • Yasminar Amaerita Telaumbanua Universitas Nias
  • Angelin Marpaung Universitas Nias
  • Ceria Putri Damai Gulo Universitas Nias
  • Dodi Kardo Wijaya Waruwu Universitas Nias
  • Erika Zalukhu Universitas Nias
  • Novita Purnawirati Zai Universitas Nias

DOI:

https://doi.org/10.59934/jaiea.v4i1.560

Keywords:

DeepL Translate, Google Translate, Machine Translation, Translation accuracy

Abstract

DeepL Translate and Google Translate are two leading machine translation tools. The focus of this research is to analyze the accuracy of translation results provided by DeepL Translation and Google Translation specifically in translating English to Indonesian. This research used a qualitative approach of document analysis and interviews. The advanced neural machine translation technology of DeepL, by utilizing extensive data, enables it to recognize language nuances and provide contextually accurate translations. In contrast, Google Translate, despite having grown to be supported by hundreds of languages, often struggles with complex sentences and idiomatic expressions. DeepL's accuracy and natural-sounding translations make it a top choice for professional and detailed translations. The study concludes that DeepL's focus on quality and accuracy, rather than the breadth of language support, makes it a more reliable translation tool.

Downloads

Download data is not yet available.

References

Agung, I.G.A.M et al (2023). Translation Performance of Google Translate and DeepL in Translating Indonesian Short Stories Into English.

Bahruddin (2023). Accuracy Unveiled: A Closer Look at Google Translate and DeepL. Volume: 3

Sidiq dan Syafrudin (2024). Students’ Perception of Using DeepL for Translating English text. Volume 12 No 1. Pages 139-148.

Creswell, J.W (2014). Research Design : Qualitative, Quantitative, and Mixed Method Approaches. 4th Edition.

Fitra,T.N (2023). Performance of Google Translate, Microsoft Translator, and DeepL Translator: Error Analysis of Translation Result. Volume 8.

Miles, M.B et al (2014). Qualitative Data Analysis: A Methods Sourcebook. Third Edition Salsabila, K.A et al (2024). The Students’ Perception on the Use of “DeepL Translation Tool” for Reading Comprehension at FKIP Universitas Riau. Volume 11.

Nas, T.W (2022). An Analysis on Students Dependecy in Using Google Translate Application of The Fifth Semester at English Language Education Study Program of FKIP UIR. Thesis.

Salinas, M.J.V & Burbat, R (2023). Google Translate and DeepL: breaking taboos in translator training. Observational study and analysis. Pages 243-266.

Sebo,P & de Lucia, S (2024). Performance of machine translators in translating French medical research abstracts to English: A comparative study of DeepL, Google Translate, and CUBBITT.

Downloads

Published

2024-10-15

How to Cite

Telaumbanua, Y. A., Marpaung, A., Gulo, C. P. D., Waruwu, D. K. W., Zalukhu, E., & Zai, N. P. (2024). Analysis of Two Translation Applications : Why is DeepL Translate more accurate than Google Translate?. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 4(1), 82–86. https://doi.org/10.59934/jaiea.v4i1.560