Abstract
Ethnocultural units are the most important components of a language that express national and cultural characteristics and specific meanings. Currently, with the rapid development of globalization and artificial intelligence (AI) technologies, it is important to accurately convey these units in AI language models. However, due to the complexity of their semantics and pragmatics, there are excessive deviations in the translation and interpretation of these units. Consequently, this can lead to a distortion of meaning and a decrease in the quality of interaction with AI. The purpose of the article is to study and analyze the quality of machine translations when translating ethnocultural units in artificial intelligence systems. In addition, to systematize these deviations for further optimization of language models in translation using neural networks, to provide recommendations for the correct transmission of ethnocultural units. To analyze existing artificial intelligence systems and their methods of processing ethnocultural units. This also contributes to improving the quality of intercultural communication. To comprehensively study the topic, a comparative analysis of the results of translating ethnocultural units with various AI systems and people is carried out. Translation quality is tested in Python. The quality of translation of texts of different styles, as well as ethnocultural units in different languages and cultures, is compared, and the final indicators of the quality assessment mechanisms for machine translation are presented to determine the meaning and contextual application of the translation. Improving artificial intelligence in the field of working with ethnocultural units allows improving intercultural communication, especially in the context of globalization and the growing need for multilingual information.