Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features

• URL: http://arxiv.org/abs/2409.04009v1
• Date: Fri, 6 Sep 2024 03:28:38 GMT
• Title: Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features
• Authors: Miao Fan, Yeqi Bai, Mingming Sun, Ping Li,
• Abstract summary: Relation classification (RC) plays a pivotal role in both natural language understanding and knowledge graph completion. Conventional approaches on RC, regardless of feature engineering or deep learning based, can obtain promising performance on categorizing common types of relation. 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.
• Score: 30.11073476165794
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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