  Neural relation extraction models have shown promising results in recent
years; however, the model performance drops dramatically given only a few
training samples. Recent works try leveraging the advance in few-shot learning
to solve the low resource problem, where they train label-agnostic models to
directly compare the semantic similarities among context sentences in the
embedding space. However, the label-aware information, i.e., the relation label
that contains the semantic knowledge of the relation itself, is often neglected
for prediction. In this work, we propose a framework considering both
label-agnostic and label-aware semantic mapping information for low resource
relation extraction. We show that incorporating the above two types of mapping
information in both pretraining and fine-tuning can significantly improve the
model performance on low-resource relation extraction tasks.
