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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 1–10 of 23 papers

TitleStatusHype
Few-Shot Document-Level Relation ExtractionCode1
Towards Realistic Few-Shot Relation ExtractionCode1
Matching the Blanks: Distributional Similarity for Relation LearningCode1
Diversity Over Quantity: A Lesson From Few Shot Relation Classification—0
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features—0
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation LearningCode0
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification—0
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain AdaptationCode0
Dependency-aware Prototype Learning for Few-shot Relation ClassificationCode0
Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text—0
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