Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems
This work addresses the preservation of cultural identity in translation for Indigenous, regional, and low-resource languages, representing a domain-specific advancement in NLP.
The paper tackles the problem of AI translation models failing to capture cultural nuances, which marginalizes linguistic diversity, by proposing a multi-agent AI framework for culturally adaptive translation; it outperforms GPT-4o in producing contextually rich and culturally embedded translations for underserved language communities.
Language is a cornerstone of cultural identity, yet globalization and the dominance of major languages have placed nearly 3,000 languages at risk of extinction. Existing AI-driven translation models prioritize efficiency but often fail to capture cultural nuances, idiomatic expressions, and historical significance, leading to translations that marginalize linguistic diversity. To address these challenges, we propose a multi-agent AI framework designed for culturally adaptive translation in underserved language communities. Our approach leverages specialized agents for translation, interpretation, content synthesis, and bias evaluation, ensuring that linguistic accuracy and cultural relevance are preserved. Using CrewAI and LangChain, our system enhances contextual fidelity while mitigating biases through external validation. Comparative analysis shows that our framework outperforms GPT-4o, producing contextually rich and culturally embedded translations, a critical advancement for Indigenous, regional, and low-resource languages. This research underscores the potential of multi-agent AI in fostering equitable, sustainable, and culturally sensitive NLP technologies, aligning with the AI Governance, Cultural NLP, and Sustainable NLP pillars of Language Models for Underserved Communities. Our full experimental codebase is publicly available at: https://github.com/ciol-researchlab/Context-Aware_Translation_MAS