Application of self-supervised learning in natural language processing

Abstract

Self-supervised learning uses the label-free data learning model and has a significant impact on the NLP task. It reduces data annotation costs and improves performance. The main applications include pre-training models such as BERT and GPT, contrast learning, and pseudo-supervised and semi-supervised methods. It has been successfully applied in text classification, emotion analysis and other fields. Future research directions include mixed unsupervised learning, cross-modal learning and improving interpretability of models while focusing on ethical social issues.