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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

Showing 110 of 23 papers

TitleStatusHype
Matching the Blanks: Distributional Similarity for Relation LearningCode1
Towards Realistic Few-Shot Relation ExtractionCode1
Few-Shot Document-Level Relation ExtractionCode1
Multi-Level Matching and Aggregation Network for Few-Shot Relation ClassificationCode0
Dependency-aware Prototype Learning for Few-shot Relation ClassificationCode0
Meta-Information Guided Meta-Learning for Few-Shot Relation ClassificationCode0
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art EvaluationCode0
FewRel 2.0: Towards More Challenging Few-Shot Relation ClassificationCode0
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation LearningCode0
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain AdaptationCode0
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