Operator words versus MT systems and LLMs

This paper investigates the meanings of two classes of Russian discourse words, defined by modal operators VER and AFF, and examines their translation equivalents in nine target languages using machine translation (MT) systems, large language models (LLMs), and human expert translations. VER (verification) indicates confirmation of a hypothesis, while AFF (affirmation) expresses a strengthened belief in a hypothesis. The study uses a set of 17 Russian discourse words and evaluates their translations in English, German, Danish, Swedish, Icelandic, Ukrainian, Bulgarian, Ossetiс, and Arabic across 85 test sentences. The primary goal was to test the universality of the VER and AFF distinction, hypothesizing that these classes remain distinct across languages despite the lack of direct one-toone translation equivalents. The study assumed that VER and AFF operators, corresponding to DE RE and DE DICTO attitudes respectively, differ fundamentally in semantics and distributional behavior. The study confirms the semantic and distributional independence of VER and AFF operators, supporting their universality. LLMs, despite not being specialized for MT tasks, showed remarkable adaptability and context awareness compared to traditional MT systems. The findings highlight the potential of LLMs in nuanced translation tasks and underscore the complexity of translating modal discourse words. Future work will explore custom models and further refine evaluation metrics for translation accuracy