Key Challenges in AI-Powered Translation in the Context of the Georgian Language

The given article examines key challenges facing AI-powered translation systems when processing Georgian, a language with unique linguistic and cultural characteristics but referred to as a low-resource language in terms of the lack of computerized data. The study identifies two primary categories of translation barriers: cultural and linguistic challenges.

Cultural challenges include mistranslation of culturally specific concepts, inadequate handling of Georgian idioms and proverbs, and failure to capture figurative language. Linguistic challenges stem from Georgian's agglutinative nature, ergative case system, flexible word order, and polypersonal agreement patterns. Georgian verbs encode subject, object, tense, aspect, and directionality in single morphological units, creating difficulties for AI systems trained primarily on structurally different languages.

Current neural machine translation systems, including Google Translate and DeepL, consistently struggle with Georgian due to limited training data and inadequate handling of morphological complexity, resulting in awkward or culturally inappropriate translations. The findings highlight the need for specialized NLP approaches for morphologically complex, low-resource languages and emphasize the importance of incorporating cultural context into AI translation systems.