Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision

05/18/2022

∙

by   Zhen Wan, et al.

∙

2

∙

share

Contrastive pre-training on distant supervision has shown remarkable effectiveness for improving supervised relation extraction tasks. However, the existing methods ignore the intrinsic noise of distant supervision during the pre-training stage. In this paper, we propose a weighted contrastive learning method by leveraging the supervised data to estimate the reliability of pre-training instances and explicitly reduce the effect of noise. Experimental results on three supervised datasets demonstrate the advantages of our proposed weighted contrastive learning approach, compared to two state-of-the-art non-weighted baselines.