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Few-Shot Relation Classification

Few-Shot Relation Classification is a particular relation classification task under minimum annotated data, where a model is required to classify a new incoming query instance given only few support instances (e.g., 1 or 5) during testing.

Source: MICK: A Meta-Learning Framework for Few-shot Relation Classification with Little Training Data

Papers

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Showing 11–20 of 23 papers

TitleStatusHype
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning—0
From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification—0
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes—0
Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification—0
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification—0
Meta-Information Guided Meta-Learning for Few-Shot Relation ClassificationCode0
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training—0
小样本关系分类研究综述(Few-Shot Relation Classification: A Survey)—0
MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data—0
FewRel 2.0: Towards More Challenging Few-Shot Relation ClassificationCode0
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