用细粒度特征提升少样本关系分类的泛化能力
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features
- 引入大间隔原型网络与细粒度特征融合
- 在FewRel数据集上显著优于多个基线模型
- 适合处理长尾关系分类场景
关系分类（RC）在自然语言理解与知识图谱补全中起关键作用，旨在识别自由文本句子中两个实体间的语义关系。传统方法在常见关系上表现良好，但因标注样本不足，难以识别大量长尾关系。本文认为少样本学习对RC具有重要实践意义，提出改进的度量学习框架LM-ProtoNet（FGF），结合大间隔原型网络与细粒度特征，以增强对长尾关系的泛化能力。在大规模监督少样本关系分类数据集FewRel上的实验表明，该方法显著优于多个基线模型。
原文摘要 · Abstract (English)
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.
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