Students’ Accuracy and Perceptions in Translating Descriptive Text with The Assistance of DEEPL Translate
DOI:
https://doi.org/10.36733/sphota.v18i1.13610Keywords:
students' perception, DeepL Translate, translation accuracy, machine translation, descriptive text.Abstract
The study investigates the accuracy of students’ translations of descriptive texts assisted by DeepL Translate and students’ perception toward the use of this translation tool in translation learning. The presence of machine translation tools in academic contexts has raised concerns regarding translation quality and students' overreliance on automated systems. Employing a descriptive qualitative design, the study involved 32 undergraduate students of English Education Study Program at Universitas Negeri Padang. Data were collected through translation tasks consisting of three descriptive texts and a perception questionnaire. Students’ translation accuracy was measured using Nababan translation quality assessment model, while students' perceptions were examined across four aspects: attractiveness, relevance, perceived usefulness, and perceived motivation. The findings show that students achieved the highest translation accuracy in texts with explicit meanings and simple sentence structures (mean score 2.75), while accuracy declined in texts containing figurative language, abstract expressions, and longer sentence constructions (mean scores 2.48 and 2.44). The overall mean score of translation accuracy was 2.55, categorized as accurate. The questionnaire results reveal that students generally hold positive perceptions toward the use of DeepL Translate, with an overall mean score of 3.02, particularly regarding its relevance (3.20) and attractiveness (3.17). However, positive perceptions were not consistently aligned with high translation accuracy. These findings suggest that although DeepL Translate can support translation learning, students’ still need strong linguistic competence and post-editing skill to ensure accurate translation outcomes.
References
Al-Sulaimaan, M. D., & Khoshaba, M. (2018). Translation Quality Assessment of Machine Translation: Google Translate of News Text from English into Arabic. International Journal of Language Academy, 6(2), 245–258.
Ardi, H., Al Hafizh, M., Rezqi, I., & Tuzzikriah, R. (2022a). Can Machine Translations Translate Humorous Texts? Humanus, 21(1), 99. https://doi.org/10.24036/humanus.v21i1.115698
Ardi, H., Al Hafizh, M., Rezqi, I., & Tuzzikriah, R. (2022b). Can Machine Translations Translate Humorous Texts? Humanus, 21(1), 99. https://doi.org/10.24036/humanus.v21i1.115698
Arnita, A., Maulina, & Rusli, T. I. (2025). EFL Students’ Perception of DeepL Translation Tool Utilization in Translating Scientific Published Research Articles. International Journal of Research in Education, 5(2), 2745–3553. https://doi.org/10.26877/ijre.v5i2.2023
Chen, Y. (2021). College students’ use and acceptance of translation tools: A mixed-methods study. Language Learning & Technology, 25(3), 195–216.
Fitria, T. N. (2023). Performance of Google Translate, Microsoft Translator, and DeepL Translator: Error Analysis of Translation Result. Al-Lisan, 8(2), 115–138. https://doi.org/10.30603/al.v8i2.3442
Gerot, & Wignell, P. (1995). Making Sense of Functional Grammar. Antipodean Educational Enterprises., 39–155. https://doi.org/10.1515/9783110800487.39
Gong, M. (2025). The neural network algorithm-based quality assessment method for university English translation. Network: Computation in Neural Systems, 36(3), 649–661. https://doi.org/10.1080/0954898X.2024.2338446
Hatim, B., & Munday, J. (2019). Translation: An advanced resource book for students. https://doi.org/10.4324/9780429266348
Kamaluddin, M. I., Rasyid, M. W. K., Abqoriyyah, F. H., & Saehu, A. (2024). Accuracy Analysis of DeepL: Breakthroughs in Machine Translation Technology. Journal of English Education Forum (JEEF), 4(2), 122–126. https://doi.org/10.29303/jeef.v4i2.681
Kirana, Amanda Kembaren, Waty, F. R., Hz, & Rahman, B. I. (2024). The Influence of DeepL Translator On EFL Students’ Writing. Jurnal Ilmu Sosial, Humaniora Dan Seni, 3(1), 746–753. https://doi.org/10.62379/jishs.v3i1.2059
Laksana & Komara. (2024). Indonesian EFL Students’ Perceptions of DeepL Machine Translation Tool: Utilization, Advantages, and Disadvantages. Journal of Language and Literature Studies, 4(2), 256–276. https://doi.org/10.36312/jolls.v4i2.1931
Läubli, S., Sennrich, R., & Volk, M. (2018). Has machine translation achieved human parity? A Case for Document-level Evaluation. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, 4791–4796. https://doi.org/10.18653/v1/d18-1512
Liu, S., & Zhu, W. (2023). An Analysis of the Evaluation of the Translation Quality of Neural Machine Translation Application Systems. Applied Artificial Intelligence, 37(1). https://doi.org/10.1080/08839514.2023.2214460
Meng, J. (2022). English Translation Ability for Cultivation Mode Based on Multimodal Auxiliary Scaffolding Teaching Method. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/3556703
Moneus, A. M., & Sahari, Y. (2024). Artificial intelligence and human translation: A contrastive study based on legal texts. Heliyon, 10(6), e28106. https://doi.org/10.1016/j.heliyon.2024.e28106
Munawwarah, M. &, Martriwati., & (2024). (2024). STUDENTS ’ PERCEPTION OF USING DEEPL AS MACHINE. 5(2), 284–295. https://doi.org/10.22236/ellter.v5i2.15813.g4779
Nababan, M., Nuraeni, A., & Sumardiono. (2012). Pengembangan Model Penilaian Kualitas Terjemahan. Kajian Linguistik Dan Sastra, 24, 39–57.
Nassaji, H. (2015). Qualitative and descriptive research: Data type versus data analysis. Language Teaching Research, 19(2), 129–132. https://doi.org/10.1177/1362168815572747
Newmark, P. (1988). A textbook of translation. In Text book of translation. In Shanghai Foreign Language Education Press.
Ni Made Verayanti Utami, & Putu Devi Maharani. (2019). Translation Techniques Used in the Translation of the Tale of Tom Kitten Done By Dharmasiswa Students of Stiba Saraswati Denpasar. SPHOTA: Jurnal Linguistik Dan Sastra, 11(2), 1–8. https://doi.org/10.36733/sphota.v11i2.1193
Pan, Y., & Zheng, Z. (2025). The Combined Effect of Grammatical Proficiency and Machine Translation Assistance on Translation Accuracy: A Quantitative Study. Proceedings of 2024 International Conference on Artificial Intelligence and Future Education, AIFE 2024, February, 141–148. https://doi.org/10.1145/3708394.3708419
Peng, X., Wang, X., & Li, X. (2024). When Student Translators Meet With Machine Translation: The Impacts of Machine Translation Quality and Perceived Self-Efficacy on Post-Editing Performance. SAGE Open, 14(4), 1–16. https://doi.org/10.1177/21582440241291624
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
Rahmania . M, & Ardi. H. (2023). Translation Techniques of Anger Expressive Utterances in the Novel The Hate U Give (2017) by Angie Thomas. SPHOTA: Jurnal Linguistik Dan Sastra, 15(1), 1–11. https://doi.org/10.36733/sphota.v15i1.5686
Sawant, S. B. (2010). Communication in professional practice. International Journal of Digital Library Services, 2(1), 59–66.
Sepesy Maučec, M., & Donaj, G. (2020). Machine Translation and the Evaluation of Its Quality. Recent Trends in Computational Intelligence, 1–20. https://doi.org/10.5772/intechopen.89063
Tavares, C., Tallone, L., Oliveira, L., & Ribeiro, S. (2023). The Challenges of Teaching and Assessing Technical Translation in an Era of Neural Machine Translation. Education Sciences, 13(6). https://doi.org/10.3390/educsci13060541
Toral, A., & Way, A. (2018). What Level of Quality Can Neural Machine Translation Attain on Literary Text? 263–287. https://doi.org/10.1007/978-3-319-91241-7_12
Yang, Y., Liu, R., Qian, X., & Ni, J. (2023). Performance and perception: machine translation post-editing in Chinese-English news translation by novice translators. Humanities and Social Sciences Communications, 10(1), 1–8. https://doi.org/10.1057/s41599-023-02285-7
Zhao, H., Liu, Y., Tao, S., Meng, W., Chen, Y., Geng, X., Su, C., Zhang, M., & Yang, H. (2024). From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN60899.2024.10650457
