The Role and Challenges of Self-Supervised Learning in Natural Language Processing

Abstract

Self-supervised learning uses label-free data to enhance machine learning models, offering significant potential for natural language processing (NLP). This paper examines its applications and challenges in NLP, including tasks like text classification, sentiment analysis, and machine translation. While methods like BERT and GPT showcase the power of self-supervised learning, issues such as data acquisition and task design remain. Despite these hurdles, the continued integration of self-supervised learning is vital for advancing NLP capabilities.