Abstract
Supervised open relation extraction aims to discover novel relations by leveraging supervised data of pre-defined relations. However, most existing methods do not achieve effective knowledge transfer from pre-defined relations to novel relations, they have difficulties generating high-quality pseudo-labels for unsupervised data of novel relations and usually suffer from the error propagation issue. In this paper, we propose a Cluster-aware Pseudo-Labeling (CaPL) method to improve the pseudo-labels quality and transfer more knowledge for discovering novel relations. Specifically, the model is firstly pre-trained with the pre-defined relations to learn the relation representations. To improve the pseudo-labels quality, the distances between each instance and all cluster centers are used to generate the cluster-aware soft pseudo-labels for novel relations. To mitigate the catastrophic forgetting issue, we design the consistency regularization loss to make better use of the pseudo-labels and jointly train the model with both unsupervised and supervised data. Experimental results on two public datasets demonstrate that our proposed method achieves new state-of-the-arts performance.- Anthology ID:
- 2022.coling-1.158
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1834–1841
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.158
- DOI:
- Cite (ACL):
- Bin Duan, Shusen Wang, Xingxian Liu, and Yajing Xu. 2022. Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1834–1841, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction (Duan et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.coling-1.158.pdf
- Code
- bobtuan/capl
- Data
- TACRED