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Few-Shot Relation Classification

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하위 태스크: 0
평가표: 4
보존된 평가 기록: 4

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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ZhixiuYe/MLMAN

공식 코드

Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification

https://github.com/nicpopovic/fredo

공식 코드

Few-Shot Document-Level Relation Extraction

https://github.com/pnborchert/core

공식 코드

CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation

https://github.com/pnborchert/multirep

공식 코드

Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning

https://github.com/thunlp/fewrel

공식 코드

FewRel 2.0: Towards More Challenging Few-Shot Relation Classification

https://github.com/uf-hobi-informatics-lab/ClinicalTransformerRelationExtraction

Matching the Blanks: Distributional Similarity for Relation Learning

https://github.com/yi-han/BERT_Relation_Extraction

Matching the Blanks: Distributional Similarity for Relation Learning

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