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

By Miao Fan, Yeqi Bai, Mingming Sun, Ping Li. 2019

38 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Haopeng Ren
2. Yi Cai
3. Guohua Wang
4. Xiaofeng Chen
5. Qing Li

Citations in the corpus

• A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification (2020, methodology): “They are listed as follows: Siamese (Koch et al., 2015), Proto (Han et al., 2018), LM-ProtoNet (Fan et al., 2019), HATT Proto (Gao et al., 2019a), MLMAN (Ye and Ling, 2019b), Proto(BERT), BERT-PAIR (Gao et al., 2019b).”