Enhance Prototypical Network with Text Descriptions for Few-shot Relation Classification

Recently few-shot relation classification has drawn much attention. It devotes to addressing the long-tail relation problem by recognizing the relations from few instances. The existing metric learning methods aim to learn the prototype of classes and make prediction according to distances between query and prototypes. However, it is likely to make unreliable predictions due to the text diversity. It is intuitive that the text descriptions of relation and entity can provide auxiliary support evidence for relation classification. In this paper, we propose TD-Proto, which enhances prototypical network with relation and entity descriptions. We design a collaborative attention module to extract beneficial and instructional information of sentence and entity respectively. A gate mechanism is proposed to fuse both information dynamically so as to obtain a knowledge-aware instance. Experimental results demonstrate that our method achieves excellent performance.