Translation, Identity, and Cultural Representation in Indian Literature

Abstract

The rapid development of multilingual artificial intelligence has expanded the possibilities for translating Indian literary texts across linguistic and cultural boundaries. However, translation quality in computational research is still assessed primarily through lexical, semantic, or sentence-level correspondence, leaving cultural identity preservation comparatively under-examined. This study proposes a culturally grounded framework for evaluating AI-assisted literary translation from selected Indian languages into English.
It focuses on whether translation systems retain identity-bearing elements such as kinship relations, caste and community references, honorifics, culturally embedded metaphors, ecological imagery, idiomatic expressions, and region-specific narrative voice. The proposed research combines multilingual neural and large-language-model translation with computational similarity measures and structured evaluation by bilingual human readers.
A Cultural Representation Preservation Index is introduced to distinguish technically fluent translations from translations that maintain culturally meaningful information. The study further examines whether discourse-aware contextual prompting and cultural gloss information improve representation without producing excessive explanatory translation or semantic distortion.