Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning
Hongru Wang,
Zhijing Jin,
Jiarun Cao
et al.
Abstract:Standard few-shot relation classification (RC) is designed to learn a robust classifier with only few labeled data for each class. 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. In this work, we define a new task, inconsistent few-shot RC, where the model needs to handle the inconsistency of N and K between training and testing. To address this new task, we propose Prototype … Show more
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