Keywords
artificial intelligence
emotional and expressive units
emotional meaning
expressiveness
pragmatic meaning
translation strategies
machine translation
translation equivalence
linguocultural studies.
How to Cite
Abstract
The rapid development of artificial intelligence has significantly influenced contemporary translation practices and created new possibilities for processing multilingual texts. At the same time, the translation of emotional and expressive language remains challenging because such language conveys not only lexical meaning but also emotional intensity, evaluation, stylistic colouring, communicative intention, and culturally specific associations. This study examines the representation of emotional and expressive units in English–Uzbek translation produced with the assistance of artificial intelligence. The main aim is to determine how effectively AI-mediated translation preserves the semantic content, emotional intensity, expressive colouring, and pragmatic meaning of emotionally marked English expressions when rendered into Uzbek. The study employs a qualitative comparative approach based on descriptive and contextual analysis. Particular attention is given to emotionally evaluative vocabulary, intensifiers, interjections, idiomatic expressions, figurative language, irony, and context-dependent expressive constructions. The analysis considers semantic correspondence, emotional equivalence, pragmatic appropriateness, stylistic naturalness, and cultural compatibility. The findings indicate that AI-based translation systems can generally reproduce the basic semantic content of emotional expressions, while the preservation of emotional intensity, expressive force, and context- dependent pragmatic meaning is less consistent. Literal rendering, reduction of emotional intensity, inappropriate lexical choices, and loss of cultural associations may affect the communicative effect of the translated text. The study argues that AI-generated translation should therefore be evaluated through a multidimensional framework that extends beyond lexical and grammatical accuracy. The findings are relevant to translation studies, AI-assisted translation, translator education, and post-editing practices in English–Uzbek translation.References
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