Meta-learning triplet contrast network for few-shot text classification

Few-shot text classification (FSTC) strives to predict classes not involved in the training by learning from a few labeled examples. Currently, most tasks construct meta-tasks in a randomized manner that fails to give more priority to hard-to-identify classes and samples. Besides, some tasks incorporated a contrast strategy, but the sample could only be compared to positive or negative examples individually. In this work, we propose a Meta-learning Triplet Contrast Network (Meta-TCN) with bidirectional contrast capability to solve the above problem. Specifically, Meta-TCN uses external knowledge with labeled information as the class examples, which decouples the embedding of prototypes from the support pool. Meanwhile, the class examples combine the support samples to construct triplet pairs used for learning. Unlike previous studies, the model can learn negative and positive knowledge simultaneously, ensuring that understanding is enriched and enhances learning. Further, we improve the shortcomings of randomness in the meta-task construction process by proposing a Dynamic Rate of Change (DRC) sampling strategy. DRC enhances the model's focus on difficult-to- classify samples. We conducted extensive experiments on six benchmark datasets such as Huffpost and RCV1. Experiments show that the average accuracy of Meta-TCN can achieve state-of-the-art performance in the vast majority of tasks.