Artificial Intelligence Translation Approaches for Endangered Language Preservation and Revitalization

Abstract

As globalization accelerates, endangered languages face increasing vulnerability from dominant world languages. This paper investigates how artificial intelligence (AI) technologies, particularly neural machine translation (NMT), can support the preservation and revitalization of endangered languages. The study examines AI translation technologies including neural machine translation, transfer learning techniques, and multilingual models that facilitate bidirectional translation between endangered and major world languages. It highlights successful applications of AI translation in creating parallel corpora, bilingual dictionaries, and cross-linguistic educational resources. The paper addresses critical challenges inherent to endangered language translation: severe data scarcity, lack of standardized orthography, complex morphological systems, and the imperative of preserving cultural nuance. Through analysis of quality assessment metrics and community-based evaluation approaches, this study emphasizes the essential role of human-AI collaboration in translation workflows. Findings indicate that while AI translation methods offer promising pathways for language preservation, success requires culturally sensitive model development, appropriate quality standards, and most critically, community ownership of both processes and resources to ensure meaningful and sustainable outcomes.