Non-English Department Students’ Perceptions of Using Chat GPT for English Texts Translation
DOI:
https://doi.org/10.36456/y15qx131Keywords:
ChatGPT, Non-English Department, Students’ perception, TranslationAbstract
This study aims to examine non-English students' perceptions of utilizing ChatGPT for translating English texts. In the modern era, where individuals from all backgrounds converge to share knowledge, there is a growing necessity for precise translation of English texts. The formulation of the research question is as follows: What is the perception of non-English department students regarding ChatGPT as a tool for translating English text? This study employs descriptive qualitative methodology. Using ChatGPT for translation, seventh-semester non-English students at Universitas PGRI Adi Buana Surabaya were observed in order to collect the data. The interviews were conducted at Universitas PGRI Adi Buana Surabaya with five randomly chosen students, including 2 females and 3 males. The researcher conducted individual interviews with each of the five students, and the conversations were recorded as documentation of the research undertaken at the university. The data are then assessed thematically. The research findings indicate that the application of ChatGPT for translating English texts for non-English department students, from the students' viewpoint, presents a varied environment, encompassing: 1) User-Friendly and Accessible; 2) Speed versus Accuracy; 3) Striking a Balance Between User-Friendliness and Accuracy; 4) Adaptability in Translation Style Flexibility; and 5) Utilization Recommendations. This research provides significant insights for educators, educational technology developers, and others seeking to enhance digital tools such as ChatGPT.
References
Abuissac, S. R. S., Sapar, A. A. Bin, & Saged, A. A. G. (2020). Pragmatic Interpretation and Translational Equivalence of Ironic Discourse in the Holy Qurʾan Based on SAT and EAT Theories: Arberry’s English Translation as a Case Study. https://api.semanticscholar.org/CorpusID:219669415
Aldrich, J. (2021). "User Satisfaction and Accessibility in Translation Tools." Journal of Linguistics Technology, 24(3), 123-145.
Arfanti, Y., Sinar, T. S., Bell, Z. I., & Lubis, S. (2018). The Explicit and Implicit Meaning Potential Can Change in the Process of Translation from the Indonesian Text to the English Text in Tempo Magazines. IOSR Journal of Humanities and Social Science, 19, 133–138. https://api.semanticscholar.org/CorpusID:56402296
Artetxe, M., et al. (2020). "Compounding the Performance Improvements of Asymmetric Re-ranking." arXiv preprint arXiv:2004.09813.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Brown, P., et al. (2019). "Machine Translation Challenges: Navigating the Trade-off Between Speed and Accuracy." Journal of Artificial Intelligence Research, 45, 321-345.
Castillo, A. G. R., Serna, G. J., Silva, Arocutipa, J. P. F., Berrios, H. Q., Rodriguez, M. A. M., Reyes, G. Y., Lopez, H. R. P., Teves, R. M. V., Herbert, Rivera, V. H., & Arias-Gonzáles, J. L. (2023). Effect of Chat GPT on the digitized learning process of university students. Journal of Namibian Studies : History Politics Culture. https://api.semanticscholar.org/CorpusID:259448577
Castillo-González, W., López, C. O., & Bonardi, M. C. (2022). Chat GPT: a promising tool for academic editing. Data & Metadatahttps://api.semanticscholar.org/CorpusID:260987570 Devlin, J., et al. (2018). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." arXiv preprint arXiv:1810.04805.
Esfeld, M. (2000). Aristotle’s Direct Realism In De Anima. Review of Metaphysics, 54, 321–336. https://api.semanticscholar.org/CorpusID:169679626
Farkhan, M. (2018). Translation strategies of Islamic terms in Indonesian-English research papers. https://api.semanticscholar.org/CorpusID:59497348
Ghazvininejad, M., Levy, O., Liu, Y., & Zettlemoyer, L. (2019). Mask-Predict: Parallel Decoding of Conditional Masked Language Models. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 6111–6120. https://doi.org/10.18653/v1/D19-1633
Hayadi, A., Maisarah, I., & Soy, S. (2022). English-Indonesian Translation Methods in the Story of “Malin Kundang.” English Education : English Journal for Teaching and Learning. https://api.semanticscholar.org/CorpusID:255727000
Holt, E. B. (1904). Dr. Montague’s Theory of Time-Perception https://api.semanticscholar.org/CorpusID:149668084
Johnson, A., & Tan, B. (2019). "User Experience and Acceptance of ChatGPT among Indonesian Speakers: A Study of Diverse Educational Backgrounds." Language and Technology Review, 15(2), 89-104.
Johnson, M., et al. (2020). "The Impact of User-Friendly Translation Tools on Work Efficiency."International Journal of Applied Linguistics, 18(2), 87-105.
Lee, H., & Nurhadi, A. (2018). "The Educational Impact of Integrating ChatGPT as a Supplementary Tool for Language Learners in Indonesia." Journal of Language Education and Technology, 10(1), 55-68.
Lample, G., & Conneau, A. (2019). "Cross-lingual Language Model Pretraining." Advances in Neural Information Processing Systems, 32.
Masduki, M., & Misnadin, M. (2022). Genre Analysis of The Message Segments of An Indonesian English Tourism Text Translation: Case in Madura. Eralingua: Jurnal Pendidikan Bahasa Asing Dan Sastra. https://api.semanticscholar.org/CorpusID:258736598
Melati, E. (2019). Fostering reading comprehension ability in non-English department students through ok5r activities and cooperative learning. https://api.semanticscholar.org/CorpusID:197750929
Nurhanifah, N. D., & Haryanti, M. Hum. D. (2019). An Analysis Of English-Indonesian Translation Methods In Products’ Label. https://api.semanticscholar.org/CorpusID:204363655
Puspita, D. A., & Hartono, R. (2020). The Accuracy of the English-Indonesian Translation of Cultural Terms in Hosseini’s A Thousand Splendid Suns. https://api.semanticscholar.org/CorpusID:231545860
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. https://api.semanticscholar.org/CorpusID:160025533
Radford, A., et al. (2019). "Language Models are Few-Shot Learners." Advances in Neural Information Processing Systems, 32.
Rahmat, S., & Utomo, R. (2022). "Perceptions and Trust Levels Associated with ChatGPT-Generated Translations among Non-English Department Students." Communication and Language Studies Journal, 28(3), 215-230.
Rogers, A. (2021). Rogers, Andrew, and Gibson, Ian. Emotional Impact of Musical/Visual Synchrony Variation in Film. Original Citation Rogers, Andrew and Gibson, Ian (2012). Emotional Impact of Musical/Visual Synchrony Variation in Film. In: Proceedings of the 12th International Conference on Music Perception and Cognition. https://api.semanticscholar.org/CorpusID:253481624 Sari, L., & Lim, K. (2021). "A Comparative Study of ChatGPT and Human Translators for English-Indonesian Translation: Quality Assessment and User Preferences." Translation Studies International, 25(4), 389-408.
Smith, J. et al. (2020). "Exploring ChatGPT: Translating English Texts into Indonesian and User Perceptions." International Journal of Language Technology, 42(3), 287-304.
Shobikah, N. (2019). Grammatical Errors of Indonesian-English Text Translation in The Second Semester Students of the Arabic Study Program at FTIK IAIN Pontianak. At-Turats, 13, 79–93. https://api.semanticscholar.org/CorpusID:213564404
Sipahutar, S. R., Marpaung, E. M., Sianturi, E. R., & Rambe, K. R. (2021). KINDS OF TRANSLATION METHOD USED BY THE STUDENT’S IN TRANSLATING DESCRIPTIVE TEXT FROM ENGLISH TO INDONESIAN. JOURNAL BASIS. https://api.semanticscholar.org/CorpusID:234810839
