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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
Matching the Blanks: Distributional Similarity for Relation LearningCode1
Towards Realistic Few-Shot Relation ExtractionCode1
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning—0
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification—0
Diversity Over Quantity: A Lesson From Few Shot Relation Classification—0
From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification—0
Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification—0
Cross Domain Few-Shot Learning via Meta Adversarial Training—0
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification—0
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