The rapid advancement of artificial intelligence (AI) has significantly transformed the field of translation, particularly in the context of cross-linguistic communication. AI-based translation systems, including neural machine translation (NMT), have improved accessibility and efficiency in multilingual information exchange. However, despite these technological advancements, numerous challenges persist, especially when translating texts from English into structurally and culturally diverse languages. This study aims to analyze the linguistic, cultural, and technological challenges associated with AI-driven translation. The research employs a qualitative analytical approach, examining typical translation outputs and identifying recurring issues such as semantic ambiguity, contextual misinterpretation, and cultural mismatch. The findings reveal that while AI systems demonstrate high performance in handling general language structures, they struggle with idiomatic expressions, stylistic nuances, and culturally embedded meanings. The study highlights the necessity of integrating linguistic knowledge and human expertise into AI systems to improve translation quality and reliability.
Ablyaeva Venera (Mon,) studied this question.
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