Cross-Lingual Transfer Learning: Applications in Low-Resource Languages

With the acceleration of globalization and the rapid advancement of information technology, cross-lingual natural language processing has become a prominent research focus. However, most languages worldwide remain low-resource and lack sufficient annotated data to train high-quality models. Cross-lingual transfer learning is an effective approach to alleviate data scarcity. It transfers knowledge learned in high-resource languages to low-resource languages and significantly improves performance on downstream tasks with little or no target-language supervision. This paper systematically reviews applications of cross-lingual transfer learning for low-resource languages with a focus on machine translation, text classification, and named entity recognition. It synthesizes technical approaches, identifies persistent challenges, and outlines future directions. Through a comprehensive analysis of the literature, this paper summarizes key techniques and application outcomes in cross-lingual transfer learning, providing researchers with practical insights and recommendations to guide future research and deployment. The goal is to promote the broader application and development of cross-lingual NLP technologies for low-resource languages.