Machine Intelligence in Translation Studies

Author:

Acay Berra Nur1ORCID,Küçük Tuğba Nur2ORCID,Yardımcı Aylin3ORCID,Hancı-Azizoglu Eda Başak2ORCID

Affiliation:

1. Cappadocia University, Turkey

2. Mediterranean (Akdeniz) University, Turkey

3. Kahramanmaras Sutcu Imam Unıversity, Turkey

Abstract

Translation has had the effect of introducing texts into new contexts, which is praised for opening up literature and other cultural artifacts to new audiences. Now that machine translation's capabilities have rapidly advanced to deliver and explain the context of the receiver part of the communication, it has become useful to humans in this modern era. Although there has been research on machine translation that has so far concentrated on the issues in machine learning, there is a lack of research paying attention to cultural aspects of translation with MT tools. Yet, the changes in the perception of language and culture along with the recent developments in technology have led to the question of whether AI tools are able to grasp the significance of culture in communication to be able to deliver the real meaning behind the context. Thus, the purpose of this applied linguistics study is to explore how transformative digital approaches may contribute to artificially intelligent language teaching styles by overcoming the weakness of the 21st century educational systems.

Publisher

IGI Global

Reference45 articles.

1. Akorbi. (2024, July 11). The Risk of Cultural Insensitivity in AI Translations. Akorbi. https://akorbi.com/blog/the-risk-of-cultural-insensitivity-in-ai-translations/

2. Analytic Alpha. (n.d.). Artificial Intelligence in Language, Culture & Literature Teaching. Analytic Alpha. https://www.analyticalpha.nl/artificial-intelligence-in-language-culture-literature-teaching/

3. Is that a fish in your ear?: translation and the meaning of everything

4. Machine culture

5. Castilho, S., Doherty, S., Gaspari, F., & Moorkens, J. (2018). Approaches to human and machine translation quality assessment. Translation quality assessment: From principles to practice, 9-38.

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