  Relation extraction (RE), which has relied on structurally annotated corpora
for model training, has been particularly challenging in low-resource scenarios
and domains. Recent literature has tackled low-resource RE by self-supervised
learning, where the solution involves pretraining the entity pair embedding by
RE-based objective and finetuning on labeled data by classification-based
objective. However, a critical challenge to this approach is the gap in
objectives, which prevents the RE model from fully utilizing the knowledge in
pretrained representations. In this paper, we aim at bridging the gap and
propose to pretrain and finetune the RE model using consistent objectives of
contrastive learning. Since in this kind of representation learning paradigm,
one relation may easily form multiple clusters in the representation space, we
further propose a multi-center contrastive loss that allows one relation to
form multiple clusters to better align with pretraining. Experiments on two
document-level RE datasets, BioRED and Re-DocRED, demonstrate the effectiveness
of our method. Particularly, when using 1% end-task training data, our method
outperforms PLM-based RE classifier by 10.5% and 6.1% on the two datasets,
respectively.
