AMIMC: Based on Improved Meta-Learning Contrast Network with Attention Mechanism for Few Shot Text Classification

Currently, meta-learning is the mainstream approach to solving the problem of scarce data in few-shot text classification. Still, challenges remain, such as embedding vectors not being compact enough, suboptimal meta-task sampling strategies, and the rigidity of convolution operations in traditional Text CNNs. To address this, we propose the Attention Mechanism-based Improved Meta-learning Contrast Network (AMIMC), which enhances intra-class aggregation, increases inter-class separation and improves embedding quality. Additionally, Double Dynamic Similar Sampling (DDSS) generates more challenging meta-tasks, and the attention mechanism enhances the flexibility of Text CNNs, significantly boosting accuracy on five few-shot text classification datasets.