Overview of Research on Low-Resource Language Machine Translation Based on Artificial Intelligence

Zukang Yang

Abstract: This study examines the role of artificial intelligence in low-resource language translation, focusing on preserving endangered languages and cultural heritage. It reviews foundational Neural Machine Translation (NMT) theories, evaluates advancements in key technologies like data augmentation and transfer learning, and assesses their impact on translation quality. Given the scarcity of machine translation methods for low-resource languages, the paper reviews current research in multimodal, paragraph-level, and multilingual machine translation, offering insights for future studies. Case studies are included to demonstrate these technologies' practical applications and challenges. Finally, the paper explores opportunities and challenges NMT models face in low-resource contexts, aiming to enhance translation quality and support cultural heritage preservation.