HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction
Dongyang Li, Taolin Zhang, Nan Hu, Chengyu Wang, Xiaofeng He · Findings of the Association for Computational Linguistics: ACL 2022 · 2022
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 denoising 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 multihead 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 Hi-CLRE significantly outperforms strong baselines in various mainstream DSRE datasets.1 * D. Li and T.