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Few-Shot Relation Classification
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
Few-Shot Document-Level Relation Extraction
Cross Domain Few-Shot Learning via Meta Adversarial Training
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes
Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
Matching the Blanks: Distributional Similarity for Relation Learning
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
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