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This paper reviews cross-lingual transfer learning techniques for low-resource languages, focusing on applications in machine translation, text classification, and named entity recognition. It addresses the problem of data scarcity in low-resource languages by leveraging knowledge from high-resource languages. The review synthesizes technical approaches, identifies challenges, and outlines future directions, providing practical insights for researchers.
Unlock NLP for the world's under-represented languages: this survey distills practical techniques in cross-lingual transfer learning to boost performance in low-resource settings.
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.