Applications and Challenges of ChatGPT in Translation: A Multidimensional Exploration of Theory and Practice

This study aims to explore the potential and limitations of ChatGPT in translation, focusing on its application in Neural Machine Translation (NMT). By combining theoretical analysis with empirical research, the study evaluates ChatGPT’s strengths and weaknesses. It reveals ChatGPT’s superior performance in handling technical documents with high translation quality and efficiency. However, its limitations become evident in addressing cultural nuances and emotional expressions, where semantic deviation or cultural loss often occurs. Moreover, ChatGPT struggles with creative translation, failing to convey the artistic style and emotional depth of original texts, such as literary works and advertisements. The study proposes optimized paths for human-machine collaboration, emphasizing the crucial role of human translators in cultural adaptation and quality assurance. It suggests incorporating multimodal data, dynamic feedback mechanisms, and pragmatic reasoning techniques to enhance machine translation capabilities. The findings conclude that while ChatGPT serves as an efficient translation tool, complex tasks require human-machine synergy to achieve high-quality cross-cultural communication.

Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. International Conference on Learning Representations (ICLR). San Diego, CA, USA.

Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M. (2016). Neural versus phrase-based machine translation quality: A case study. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 257-267). Austin, Texas.

Castilho, S., Moorkens, J., Gaspari, F., Calixto, I., Tinsley, J., & Way, A. (2018). Is neural machine translation the new state of the art? The Prague Bulletin of Mathematical Linguistics, 110(1), 109-120.

Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1), 10345.

Guerberof-Arenas, A., & Moorkens, J. (2023). Ethics and machine translation: The end user perspective. In H. Moniz and C. Parra Escartín (Eds.), Towards responsible machine translation (pp. 113-133). New York: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-031-14689-3_7

Koehn, P. (2020). Neural machine translation. Cambridge, UK: Cambridge University Press.   

Nousias, A. (2023). The ethics of machine translation. In H. Moniz and C. Parra Escartín (Eds.), Towards responsible machine translation (pp. 29-48). New York: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-031-14689-3_3

Popović, M. (2015). Challenges in automatic translation evaluation. In Proceedings of the Tenth Workshop on Statistical Machine Translation (pp. 1-6). Lisbon, Portugal.

Pym, A. (2012). Translation ethics. London: Routledge. 

Shieber, S. M. (1994). Lessons from a restricted Turing test. Communications of the ACM, 37(6), 70-78.

Toral, A., & Sánchez-Cartagena, V. M. (2017). A multifaceted evaluation of neural versus phrase-based machine translation for 9 language directions. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (pp. 1063-1073). Valencia, Spain.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30, pp. 6000-6010). Retrieved from https://dl.acm.org/doi/10.5555/3295222.3295349