LINGUISTIC DIFFERENCES BETWEEN HUMAN AND AI TRANSLATION
Keywords:
human translation, AI translation, machine translation, neural networks, linguistics, semantics, syntax, pragmatics, translation studies, natural language processingAbstract
This article dives into the linguistic differences between human translators and artificial intelligence (AI) in today's language tech world. We'll be looking at what each does best and where they struggle, considering things like meaning, sentence structure, how well they adapt to different cultures, how they handle idioms, and their understanding of context. Human translators bring a lot to the table – their minds, their understanding of culture, and their emotions – which helps them accurately interpret the meaning and style of the text. On the other hand, AI translation is super fast and cost-effective, thanks to machine learning and neural networks. But, AI often trips up on things like ambiguity, figures of speech, and cultural nuances. This article reviews what experts have written about machine translation and human language skills. It uses comparison to look at translation examples from different types of texts. The findings show that while AI translation has gotten a lot better at being accurate and flowing smoothly, human translation still shines when it comes to creative, literary, and culturally sensitive communication. The research concludes that AI should be seen as a helpful tool, not a replacement for human translators.
References
1. Bahdanau, D., Cho, K., Bengio, Y. Neural Machine Translation by Jointly Learning to Align and Translate. — Proceedings of ICLR, 2015. — 15 p.
2. Baker, M. In Other Words: A Coursebook on Translation. — London: Routledge, 2018. — 400 p.
3. Catford, J. C. A Linguistic Theory of Translation. — London: Oxford University Press, 1965. — 103 p.
4. Hutchins, W. J. Machine Translation: Past, Present, Future. — Chichester: Ellis Horwood, 1986. — 382 p.
5. Jurafsky, D., Martin, J. H. Speech and Language Processing. — New York: Pearson Education, 2023. — 720 p.
6. Koehn, P. Statistical Machine Translation. — Cambridge: Cambridge University Press, 2010. — 433 p.
7. Newmark, P. A Textbook of Translation. — New York: Prentice Hall, 1988. — 292 p.
8. Nida, E. A. Toward a Science of Translating. — Leiden: Brill Academic Publishers, 1964. — 331 p.
9. Vaswani, A., Shazeer, N., Parmar, N., et al. Attention Is All You Need. — Advances in Neural Information Processing Systems, 2017. — Vol. 30. — P. 5998–6008.
10. Venuti, L. The Translator’s Invisibility. — London: Routledge, 1995. — 353 p.
