# Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training

arXiv: https://arxiv.org/abs/2106.10835 · HTML: https://mlsanity.com/paper/2106.10835/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 21 Jun 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction](https://mlsanity.com/paper/1909.13078/) (2019)
- [Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions](https://mlsanity.com/paper/1904.00143/) (2019)
- [DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction](https://mlsanity.com/paper/1805.09929/) (2018)
- [Are Noisy Sentences Useless for Distant Supervised Relation Extraction?](https://mlsanity.com/paper/1911.09788/) (2019)
- [Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction](https://mlsanity.com/paper/1812.10604/) (2018)
- [Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction](https://mlsanity.com/paper/1904.09331/) (2019)

## Built on by (most cited first, 8 of 8)

- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2202.13352/) (2022)
- [Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data](https://mlsanity.com/paper/2305.03827/) (2023)
- [Distantly-Supervised Joint Extraction with Noise-Robust Learning](https://mlsanity.com/paper/2310.04994/) (2023)
- [Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction](https://mlsanity.com/paper/2310.18912/) (2023)
- [From Consensus to Disagreement: Multi-Teacher Distillation for Semi-Supervised Relation Extraction](https://mlsanity.com/paper/2112.01048/) (2021)
- [READ: Improving Relation Extraction from an ADversarial Perspective](https://mlsanity.com/paper/2404.02931/) (2024)
- [SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN](https://mlsanity.com/paper/2507.06895/) (2025)

## Abstract (arXiv)

With recent advances in distantly supervised (DS) relation extraction (RE), considerable attention is attracted to leverage multi-instance learning (MIL) to distill high-quality supervision from the noisy DS. Here, we go beyond label noise and identify the key bottleneck of DS-MIL to be its low data utilization: as high-quality supervision being refined by MIL, MIL abandons a large amount of training instances, which leads to a low data utilization and hinders model training from having abundant supervision. In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels. Specifically, since VAT is label-free, we employ the instance-level VAT to recycle instances abandoned by MIL. Besides, we deploy AT at the bag-level to unleash the full potential of the high-quality supervision got by MIL. Our proposed method brings consistent improvements (~ 5 absolute AUC score) to the previous state of the art, which verifies the importance of the data utilization issue and the effectiveness of our method.