A Survey of Low-Resource Sentence Representation

Globally, with over 7, 000 languages, only about 20 are well-resourced. Low-resource languages face bottlenecks in traditional supervised learning and fine-tuning of pre-trained models due to data scarcity and informal text usage. Research on sentence representation for low-resource languages is crucial for overcoming data scarcity constraints, enhancing semantic modeling capabilities, and promoting the equitable development of natural language processing technology across the global linguistic landscape, as well as advancing various downstream task applications. This paper surveys the evolution of research in lowresource sentence representation, focusing on analyzing the core concepts, effectiveness, and limitations of traditional methods, cross-lingual knowledge transfer, and contrastive representation learning methods. It also discusses prospects for the development of low-resource sentence representation learning.