@inproceedings{wan-etal-2023-relation,
title = "Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision",
author = "Wan, Zhen and
Cheng, Fei and
Liu, Qianying and
Mao, Zhuoyuan and
Song, Haiyue and
Kurohashi, Sadao",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.195",
doi = "10.18653/v1/2023.findings-eacl.195",
pages = "2580--2585",
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: \url{https://github.com/YukinoWan/WCL}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision
%A Wan, Zhen
%A Cheng, Fei
%A Liu, Qianying
%A Mao, Zhuoyuan
%A Song, Haiyue
%A Kurohashi, Sadao
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F wan-etal-2023-relation
%X 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.
%R 10.18653/v1/2023.findings-eacl.195
%U https://aclanthology.org/2023.findings-eacl.195
%U https://doi.org/10.18653/v1/2023.findings-eacl.195
%P 2580-2585
Markdown (Informal)
[Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision](https://aclanthology.org/2023.findings-eacl.195) (Wan et al., Findings 2023)
ACL