Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision
Computation and Language
2023-02-13 v2
Abstract
Contrastive pre-training on distant supervision has shown remarkable effectiveness in 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.Our code and models are available at: https://github.com/YukinoWan/WCL
Cite
@article{arxiv.2205.08770,
title = {Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision},
author = {Zhen Wan and Fei Cheng and Qianying Liu and Zhuoyuan Mao and Haiyue Song and Sadao Kurohashi},
journal= {arXiv preprint arXiv:2205.08770},
year = {2023}
}
Comments
EACL 2023 (Findings)