A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification
Haopeng Ren, Yi Cai, Xiaofeng Chen, Guohua Wang, Qing Li · 2020
Relation Classification (RC) plays an important role in natural language processing (NLP).Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in an open environment.To incrementally recognize the novel relations, current two solutions (i.e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations.Meanwhile, prototypical network enjoys better performance on both fields of deep supervised learning and few-shot learning.However, it still suffers from the incompatible feature embedding problem when the novel relations come in.Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances meanwhile without catastrophic forgetting.Extensive experiments are conducted to evaluate the effectiveness of our proposed model.