Less is Enough: Relation Graph Guided Few-shot Learning for Multi-label Aspect Category Detection

Abstract

Few-shot Multi-label Aspect Category Detection (FMACD) is an essential task, which aims to identify multiple aspect categories in a given sentence with limited data. Recently, the prototypical network as a mainline has been used for the task due to its powerful capacity. However, existing methods mostly rely on intra-cluster samples to generate prototypes, and they struggle to extract robust prototype features in very few data cases (e.g., 1-shot). Therefore, these methods may fail to estimate label-query relevance during multi-label prediction. To solve the above issues, we propose a novel relation graph guided learning method for FMACD by considering all intra- and inter-cluster samples. Specifically, the proposed method explicitly models a relation graph to generate more robust prototypes by exploring sample relations among intra- and inter-cluster. Then, a multi-label inference strategy is proposed to enhance label-query relevance for multi-label prediction. Besides, graph contrastive learning enhances intra-cluster commonality and inter-cluster uniqueness to improve performance. Experiments show that the proposed method achieves significant performance, esp., it obtains an average of 1.55% AUC and 5.01% Macro-F1 improvement in 1-shot scenarios.