小样本关系分类研究综述

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Abstract As an important part of constructing structured knowledge, relation classification has attracted much attention in the field of natural language processing. However, in many application fields (medical and financial fields), it is very difficult to collect sufficient data for training relation classification model. In recent years, few-shot learning research which relies only on a small number of training samples is emerging in various fields. In this paper, the recent models and methods of few-shot relation classification are systematically reviewed. According to the different measurement methods, the existing methods are divided into prototype and distributed ones. According to whether using additional information, the model is divided into two categories: pretraining and non-pretraining. In addition to the regular setting of few-shot learning, we also summarize the cross domain few-shot learning and few-few-shot learning, discuss the limitations of current few-shot relation classification methods, and analyze the technical challenges faced by cross domain few-shot models. Finally, the future development of few-shot relation classification is prospected.

Key words： relation classification few-shot learning meta learning

Received: 21 February 2021

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	HU Han
	LIU Pengyuan

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HU Han,LIU Pengyuan. Few-Shot Relation Classification: A Survey[J]. , 2022, 36(2): 1-11.

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http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2022/V36/I2/1

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