Improving few-shot relation classification with multi-scale hierarchical prototype learning

Few-shot relation classification aims to distinguish different relation classes from extremely limited annotated data. Most existing methods primarily use prototype networks to construct a prototypical representation, classifying the instance by comparing its similarity to each prototype. Despite achieving promising results, the prototypes derived solely from limited support instances are often inaccurate due to constraints in feature extraction capabilities. Moreover, they ignore the different hierarchical levels of relational information, which can provide more effective guidance for classification. In this paper, we propose a novel multi-scale hierarchical prototype (Mario) learning method that captures relational interaction information at three levels: inter-set, inter-class and intra-class, enhancing the model's understanding of global semantic information and helping it distinguish subtle differences between classes. Additionally, we incorporate relational descriptive information to reduce the impact of textual expression diversity, enabling the model to emulate the human cognitive process in understanding variation. Extensive experiments conduct on the FewRel dataset demonstrate the effectiveness of our proposed model. In particular, it achieves accuracy rates of 92.52 %/95.33 %/85.46 %/91.33 % under four common few-shot settings. Notably, in the critical 5-way and 10-way 1-shot settings, it outperforms the strongest baseline by 2.87 % and 4.29 %.