Translation Technologies in the Artificial Intelligence Era: Historical Development and Classification
DOI:
https://doi.org/10.70091/Atras/vol07no01.25Keywords:
Artificial Intelligence, ChatGPT, classification, translation, translation technologiesAbstract
In a globalized world that is increasingly connected, the translation market has grown significantly. The increase in market demand has led to a growing reliance on translation technologies by human translators, who now feel obliged to use them to keep pace and meet deadlines. The development of these tools has experienced exponential growth in recent years, driven by the introduction of Artificial Intelligence into the field of translation. Mastering the different types of tools and how to handle them is essential for efficient use. In this vein, the current study first aims to examine the historical development of translation technologies. Second, it seeks to provide a new classification of translation technologies that incorporates the latest trends in the field. It employs a qualitative method that involves collecting and analyzing data through a rigorous literature review. The results show that translation technology development began many centuries ago, but the boom occurred around the 1980s. Translation technologies can be classified into two large groups: Computer-Assisted Translation tools and Machine Translation systems. The study suggests that, since Artificial Intelligence-driven tools are used in translation, Artificial Intelligence should be classified as a type of translation technology.
References
Al-Hemyari, S. H. (2023). The future of the translation profession in the light of artificial intelligence. Journal of Reproducible Research, 2(1). https://doi.org/
Bellardi, A. N., & Abidi, N. C. (2022). Advancing translation practice: A comprehensive review and classification of translation technology tools. مجلة اللسانيات, 61(2), 1–4.
Bowker, L., & Fisher, D. (2010). Computer-aided translation. In Y. Gambier & L. van Doorslaer (Eds.), Handbook of translation studies (Vol. 1). John Benjamins. https://doi.org/10.1075/hts.1.comp2
Chan, S.-W. (2012). The future of translation technologies. In Translation technologies for professional translators (pp. 1–20). Chinese University Press.
Common Sense Advisory. (2016). Market for outsourced translation and interpreting services and technology to surpass US$40 billion in 2016. https://www.commonsenseadvisory.com
DePalma, D. A. (2013). Translation supply chain management: How to build and run productive global content value chains. CSA Research.
Doherty, S. (2016). The impact of translation technologies on the process and product of translation. International Journal of Communication, 10, 947–969.
He, Y. (2011). The integration of machine translation and translation memory (Unpublished Doctoral dissertation). Dublin City University
Hutchins, J. (1996). ALPAC: The (in)famous report. MT News International, 14, 9–12.
Reprinted in S. Nirenburg, H. Somers, & Y. Wilks (Eds.), Readings in machine translation (pp. 131–135). MIT Press. (2003)
Kay, M. (1980). The proper place of men and machines in language translation. Xerox Palo Alto Research Center.
Koehn, P. (2010). Statistical machine translation. Cambridge University Press.
Lagoudaki, E. (2018). Managing new translation technologies: Translation process optimization in an evolving digital landscape. Routledge.
Mohamed, Y. A. et al. (2024). Impact of artificial intelligence on language translation: A review. IEEE Access, 12, 25553–25579. https://doi.org/10.1109/ACCESS.2024.3366802
Statista. (2023). Market size of the global language services industry from 2009 to 2022. https://www.statista.com
Stein, D. (2018). Machine translation: Past, present and future. In G. Rehm, F. Sasaki, D. Stein, & A. Witt (Eds.), Language technologies for a multilingual Europe (pp. 5–17). Language Science Press.
Wu, Y. et al. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv. https://doi.org/10.48550/arXiv.1609.08144
Zayyanu, Z. M. (2024). Explaining some fundamentals of translation technology. GAS Journal of Arts, Humanities and Social Sciences, 2(3).
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