LINGUOCULTURAL PROBLEMS IN MACHINE TRANSLATION
Keywords:
machine translation, neural machine translation, linguaculturology, cultural gap, cultural realia, phraseology, politeness strategies, cultural context, translation quality, post-editing.Abstract
The article examines the main linguacultural problems in modern neural machine translation (NMT) systems. It provides a detailed analysis of cultural gaps, culture-specific realia, symbolic and associative meanings, politeness strategies, phraseological units, and the reflection of social hierarchy. Real examples of machine translation errors are presented, along with their implications in cross-cultural communication. Current solution approaches are discussed, including cultural knowledge graphs, context-aware models, post-editing practices, and multimodal translation methods, as well as future development prospects. The conclusion emphasizes the continued necessity of human linguacultural editing for high-stakes and culturally sensitive texts.
