IPCL: Cross-domain Invariant Prototypical Contrastive Learning for Few-shot Text Classification

Prototypical Networks are a typical method in few-shot learning. The instance-based training causes the model to learn specific domain features of the support set, leading to inadequate cross-domain generalization capabilities when faced with new domain data. To this end, this paper proposes a cross-domain Invariant Prototypical Contrastive Learning (IPCL) method. We extract invariant features (content features) and abstract invariant features (emotional features) from sample characteristics to construct invariant prototypes, which are domain-independent. Based on invariant representation learning, the model decouples latent features to obtain invariant features, which form the core basis for classification decisions. Abstract invariant features, refined from variable features (style features), reveal the underlying commonalities behind these variable features. Furthermore, we propose constructing auxiliary classification tasks that directly mine invariant prototype’s semantic features, combined with metric tasks to achieve cross-task information complementarity and enhance the model’s generalization ability to new domains. Experiments show that IPCL’s decoupling of latent variables is recognizable, achieving accuracies of 92.23% and 93.45% on the ARSC and SST-2 datasets respectively. In cross-domain tasks, IPCL outperforms previous method by up to 1.11%, demonstrating IPCL’s superior cross-domain generalization capabilities.