  Distant supervision assumes that any sentence containing the same entity
pairs reflects identical relationships. Previous works of distantly supervised
relation extraction (DSRE) task generally focus on sentence-level or bag-level
de-noising techniques independently, neglecting the explicit interaction with
cross levels. In this paper, we propose a hierarchical contrastive learning
Framework for Distantly Supervised relation extraction (HiCLRE) to reduce noisy
sentences, which integrate the global structural information and local
fine-grained interaction. Specifically, we propose a three-level hierarchical
learning framework to interact with cross levels, generating the de-noising
context-aware representations via adapting the existing multi-head
self-attention, named Multi-Granularity Recontextualization. Meanwhile, pseudo
positive samples are also provided in the specific level for contrastive
learning via a dynamic gradient-based data augmentation strategy, named Dynamic
Gradient Adversarial Perturbation. Experiments demonstrate that HiCLRE
significantly outperforms strong baselines in various mainstream DSRE datasets.
