Few-Shot Relation Classification
10 papers with code • 4 benchmarks • 6 datasets
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
Benchmarks
These leaderboards are used to track progress in Few-Shot Relation Classification
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FewRel
DocRED
SciERC
FewRel 2.0
FREDo
TexRel
Latest papers with no code
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification
Human interventions to the rules for the TACRED relation \texttt{org:parents} boost the performance on that relation by as much as 26\% relative improvement, without negatively impacting the other relations, and without retraining the semantic matching component.
Cross Domain Few-Shot Learning via Meta Adversarial Training
Few-shot relation classification (RC) is one of the critical problems in machine learning.
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning
However, previous works rarely investigate the effects of a different number of classes (i. e., $N$-way) and number of labeled data per class (i. e., $K$-shot) during training vs. testing.
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes
We explore Few-Shot Learning (FSL) for Relation Classification (RC).
Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification
First, based on the prototypical networks, we propose an adaptive mixture mechanism to add label words to the representation of the class prototype, which, to the best of our knowledge, is the first attempt to integrate the label information into features of the support samples of each class so as to get more interactive class prototypes.
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification
Relation Classification (RC) plays an important role in natural language processing (NLP).
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations.
MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data
In this framework, models not only strive to classify query instances, but also seek underlying knowledge about the support instances to obtain better instance representations.
