Few-Shot Relation Classification Research Based on Prototypical Network and Causal Intervention

Abstract

To improve the accuracy of the few-shot relation classification task, the research, which weakens the influence of confounder on the performance of the model and enhances the semantic representation and feature extraction ability of the model, is carried out based on the prototypical network. And then, the weaken-confounders method based on causal intervention(WCCI) is proposed, and the RBERTI-Proto model is constructed. In WCCI, the pre-trained knowledge is stratified by the backdoor adjustment based on causal intervention, the optimal stratified number is determined by a stratified method, and the BN layer is introduced for the gradient disappearance problem. In the RBERTI-Proto model, the abilities of semantic representation and feature extraction of the model are enhanced by which the RoBERTa is used as the feature extractor of the model. Experimental results demonstrate the effectiveness of our proposed methods and the RoBERTa model as feature extractor of our model, and the ACC value of the RBERTI-Proto model achieve 93.38% on the 5-way 5-shot scenario of the FewRel dataset.

Keywords

• Few-shot learning
• causal intervention
• prototypical network
• relation classification
• RoBERTa
• confounder