Semantic Adequacy in Popular Science Texts for Young Audiences: a Comparative Analysis of Google Translate, Deepl, Chatgpt and Manus Ai Translations
Year: 2026
Type: article
Abstract: This study explores semantic adequacy in AI-based translations of popular science texts intended for young audiences. Translations produced by four systems are comparatively analyzed in terms of meaning accuracy, terminology, and completeness. The findings reveal differences in semantic performance and emphasize the importance of human supervision in ensuring reliable and precise translation.
Author: Zokirjonova, Madina
Institution: Namangan State Institute of Foreign Languages
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7155186407
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