Translation in the Age of Artificial Intelligence: Machine Translation Markers in Journalistic Texts
In the Age of Artificial Intelligence, computer-assisted translation and online translation programs have become the main focus of translation studies. The authors investigated machine-translated journalistic texts to reveal the potential of popular machine translation programs and markers of machin...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Kemerovo State University
2025-07-01
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| Series: | Виртуальная коммуникация и социальные сети |
| Subjects: | |
| Online Access: | https://jsocnet.ru/en/nauka/article/100907/view |
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| Summary: | In the Age of Artificial Intelligence, computer-assisted translation and online translation programs have become the main focus of translation studies. The authors investigated machine-translated journalistic texts to reveal the potential of popular machine translation programs and markers of machine translation in political discourse. They compared the dictum and modal content of the original text with its translations made by Yandex Translate, DeepL, and Gemini. The fragments under analysis belonged to the interview that President Vladimir Putin gave to American journalist Tucker Carlson on February 9, 2024. The interview combined stylistically labeled lexical and phraseological units with neutral and bookish vocabulary. First, the original text was analyzed for linguistic factors that could affect the translation equivalence, i.e., lexical and grammatical complexity, contextual saturation, and cultural specificity. After that, the authors identified the technological parameters of the translation algorithms that determined the translation quality. The artificial intelligence programs demonstrated different degrees of machinability of translated texts, as well as different approaches to journalistic discourse with its cultural allusions, and idioms. Gemini proved successful in conveying dictum and modal meanings while DeepL and Yandex Translate demonstrated a word-for-word strategy. |
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| ISSN: | 2782-4799 2782-4802 |