Machine Translation vs. Human Translation: A Linguistic Analysis
Keywords:
Machine translation, human translation, neural machine translation, translation quality assessment, linguistic challenges, hybrid translation modelsAbstract
Machine translation (MT) has advanced significantly with the development of neural machine translation (NMT), raising discussions about its ability to match human translation (HT). While MT systems offer speed and cost-effectiveness, they often struggle with contextual adaptation, idiomatic expressions, and syntactic variations between languages. Human translators, on the other hand, excel in linguistic nuance, cultural interpretation, and accuracy but require more time and resources. This paper examines the strengths and weaknesses of both approaches, focusing on linguistic challenges and translation quality assessment. The study also explores the role of hybrid translation models, where MT and HT complement each other to achieve efficiency and accuracy. The findings suggest that while MT is improving, it cannot yet fully replace human translation in complex and context-sensitive tasks.
References
Artetxe, M., Labaka, G., Agirre, E., & Cho, K. (2017). Unsupervised neural machine translation. arXiv preprint arXiv:1710.11041.
Britz, D., Goldie, A., Luong, M. T., & Le, Q. (2017). Massive exploration of neural machine translation architectures. arXiv preprint arXiv:1703.03906.
Cadwell, P., o’Brien, S., & Teixeira, C. S. (2018). Resistance and accommodation: factors for the (non-) adoption of machine translation among professional translators. Perspectives, 26(3), 301-321.
Chen, M. X., Firat, O., Bapna, A., Johnson, M., Macherey, W., Foster, G., ... & Hughes, M. (2018). The best of both worlds: Combining recent advances in neural machine translation. arXiv preprint arXiv:1804.09849.
Dabre, R., Chu, C., & Kunchukuttan, A. (2020). A survey of multilingual neural machine translation. ACM Computing Surveys (CSUR), 53(5), 1-38.
Forcada, M. L. (2017). Making sense of neural machine translation. Translation spaces, 6(2), 291-309.
Koehn, P. (2009). Statistical machine translation. Cambridge University Press.
Koehn, P., & Haddow, B. (2009). Interactive assistance to human translators using statistical machine translation methods. In Proceedings of Machine Translation Summit XII: Papers.
Läubli, S., Sennrich, R., & Volk, M. (2018). Has machine translation achieved human parity? a case for document-level evaluation. arXiv preprint arXiv:1808.07048.
Lavie, A., & Denkowski, M. J. (2009). The METEOR metric for automatic evaluation of machine translation. Machine translation, 23, 105-115.
Lee, S. M. (2020). The impact of using machine translation on EFL students’ writing. Computer assisted language learning, 33(3), 157-175.
Lin, C. Y., & Och, F. J. (2004). Orange: a method for evaluating automatic evaluation metrics for machine translation. In COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics (pp. 501-507).
Okpor, M. D. (2014). Machine translation approaches: issues and challenges. International Journal of Computer Science Issues (IJCSI), 11(5), 159.
Popel, M., Tomkova, M., Tomek, J., Kaiser, Ł., Uszkoreit, J., Bojar, O., & Žabokrtský, Z. (2020). Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals. Nature communications, 11(1), 1-15.
Stahlberg, F. (2020). Neural machine translation: A review. Journal of Artificial Intelligence Research, 69, 343-418.
Tu, Z., Lu, Z., Liu, Y., Liu, X., & Li, H. (2016). Modeling coverage for neural machine translation. arXiv preprint arXiv:1601.04811.
Wang, H., Wu, H., He, Z., Huang, L., & Church, K. W. (2022). Progress in machine translation. Engineering, 18, 143-153.
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., ... & Dean, J. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
Yang, S., Wang, Y., & Chu, X. (2020). A survey of deep learning techniques for neural machine translation. arXiv preprint arXiv:2002.07526.
