Task-Adaptive Feature Fusion for Generalized Few-Shot Relation Classification in an Open World Environment

Abstract

Relation Classification (RC) is an important task in information extraction. In most real-world scenarios, the frequency of relations often follows a long-tailed and open-ended distribution. However, current efforts mainly focus on the partial frequency distribution of relations, which is limited in real-world applications. Meanwhile, prototypical network achieves remarkable performance among fields of deep supervised learning, few-shot learning and open set learning. Nevertheless, in the open world environment, it still suffers from the incompatible feature embedding problem as the novel and unknown relations come in. To address these problems, we propose an Open Generalized Prototypical Network with task-adaptive feature fusion for the open generalized few-shot relation classification. Extensive experiments are conducted on public large-scale datasets and our proposed model obtains the better performances.