Natural Language Processing for Low-Resource Languages

While natural language processing (NLP) has made remarkable progress for widely-spoken languages like English and Mandarin, many of the worlds languages remain underserved by modern language technologies. This article focuses on approaches to developing NLP tools for low-resource languages.\n\nWe discuss techniques such as transfer learning from high-resource languages, unsupervised and self-supervised learning methods, and data augmentation strategies. We also explore the importance of community involvement and participatory research methods when working with speakers of low-resource languages.\n\nThe article includes case studies of successful projects in Sri Lanka and other regions, highlighting both technical and cultural considerations in this important area of research.