Threshold Dynamic Multi-Source Decisive Prototypical Network

To address the issue that prototypical networks in existing few-shot text classification methods suffer from performance limitations due to prototype shift and metric constraints, this paper proposes a meta-learning-based few-shot text classification method: Threshold Dynamic Multi-Source Decisive Prototypical Network (TDMP-Net) to solve these problems. This method designs two core components: the threshold dynamic data augmentation module and the multi-source information Decider. Specifically, the threshold dynamic data augmentation module achieves the optimization of the prototype estimation process by leveraging the multi-source information of query set samples, which thereby alleviates the prototype shift problem; meanwhile, the multi-source information Decider performs classification by relying on the multi-source information of the query set, thus alleviating the metric constraint problem. The effectiveness of the proposed method is verified on four benchmark datasets: under the five-way one-shot and five-way five-shot settings, TDMP-Net achieves average accuracies of 78.3% and 86.5%, respectively, which are an average improvement of 3.3 percentage points compared with current state-of-the-art methods. Experimental results show that this TDMP-Net can effectively alleviate the prototype shift problem and metric constraint problems, and has stronger generalization ability.