Applications and Challenges of Meta-Learning in Natural Language Processing

Abstract. With the emergence and development of meta-learning algorithms, their remarkable advantages in few-shot learning (FSL) scenarios have led to the increasing application of meta-learning in natural language processing (NLP). However, meta-learning is not without limitations, and various challenges may arise during its practical application. This paper begins by providing a brief introduction to the concept of meta-learning and delves into its complex applications in NLP, such as federated learning (FL), cross-task generalization, and few-shot text classification. The research demonstrates that the advantages of meta-learning in few-shot learning can significantly aid these applications, particularly in scenarios where data scarcity necessitates rapid adaptation. Nevertheless, throughout these studies, meta-learning also exhibits limitations in various contexts. Finally, this paper summarizes the content, analyzes the interrelationships and potential connections between the topics, and highlights current challenges in the field as well as possible future directions for development.