External Knowledge Enhancing Meta-learning Framework for Few-Shot Text Classification via Contrastive Learning and Adversarial Network

The recent methods based on meta-learning have been applied to few-shot text classification tasks, and have achieved remarkable performance, such as prototypical networks and so on. The primary mission of few-shot text classification is to learn a high-quality embedding representation for each class. However, due to the randomness in sample sampling, the representations of class prototypes often tend to be unstable. This paper proposes the SCLAWM model, which employs a combination of external knowledge and sample representations to enhance the embedding quality of class prototypes. Based on the effectiveness of contrastive learning, this paper introduces a method of supervised contrastive learning to further enhance the similarity between samples and their class prototypes. Furthermore, this paper employs an adversarial network to enhance the model’s generalization performance. The experiments show that the SCLAWM model has achieved remarkable performance on four benchmark datasets.