# Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision

arXiv: https://arxiv.org/abs/2205.08770 · HTML: https://mlsanity.com/paper/2205.08770/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://mlsanity.com/paper/2010.03851/) (2020)
- [Learning from Context or Names? An Empirical Study on Neural Relation Extraction](https://mlsanity.com/paper/2010.01923/) (2020)
- [RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://mlsanity.com/paper/1812.04361/) (2018)
- [CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning](https://mlsanity.com/paper/1911.10438/) (2019)
- [Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction](https://mlsanity.com/paper/2009.07503/) (2020)
- [CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2106.10855/) (2021)
- [WebRED: Effective Pretraining And Finetuning For Relation Extraction On The Web](https://mlsanity.com/paper/2102.09681/) (2021)

## Built on by (most cited first, 4 of 4)

- [Fine-grained Contrastive Learning for Relation Extraction](https://mlsanity.com/paper/2205.12491/) (2022)
- [Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction](https://mlsanity.com/paper/2210.11800/) (2022)
- [EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains](https://mlsanity.com/paper/2305.12924/) (2023)
- [SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN](https://mlsanity.com/paper/2507.06895/) (2025)

## Abstract (arXiv)

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