  In this paper, we reformulate the relation extraction task as mask language
modeling and propose a novel adaptive prompt-based finetuning approach. We
propose an adaptive label words selection mechanism that scatters the relation
label into variable number of label tokens to handle the complex multiple label
space. We further introduce an auxiliary entity discriminator object to
encourage the model to focus on context representation learning. Extensive
experiments on benchmark datasets demonstrate that our approach can achieve
better performance on both the few-shot and supervised setting.
