Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features
This work addresses the challenge of insufficient labeled data for long-tail relations in relation classification, which is incremental as it builds upon existing metric learning methods.
The paper tackled the problem of recognizing long-tail relations in relation classification by improving a metric learning framework for few-shot learning, achieving substantial improvements over baseline approaches on the FewRel dataset.
Relation classification (RC) plays a pivotal role in both natural language understanding and knowledge graph completion. It is generally formulated as a task to recognize the relationship between two entities of interest appearing in a free-text sentence. Conventional approaches on RC, regardless of feature engineering or deep learning based, can obtain promising performance on categorizing common types of relation leaving a large proportion of unrecognizable long-tail relations due to insufficient labeled instances for training. In this paper, we consider few-shot learning is of great practical significance to RC and thus improve a modern framework of metric learning for few-shot RC. Specifically, we adopt the large-margin ProtoNet with fine-grained features, expecting they can generalize well on long-tail relations. Extensive experiments were conducted by FewRel, a large-scale supervised few-shot RC dataset, to evaluate our framework: LM-ProtoNet (FGF). The results demonstrate that it can achieve substantial improvements over many baseline approaches.