Abstract
Open relation extraction is the task to extract relational facts without pre-defined relation types from open-domain corpora. However, since there are some hard or semi-hard instances sharing similar context and entity information but belonging to different underlying relation, current OpenRE methods always cluster them into the same relation type. In this paper, we propose a novel method based on Instance Ranking and Label Calibration strategies (IRLC) to learn discriminative representations for open relation extraction. Due to lacking the original instance label, we provide three surrogate strategies to generate the positive, hard negative, and semi-hard negative instances for the original instance. Instance ranking aims to refine the relational feature space by pushing the hard and semi-hard negative instances apart from the original instance with different margins and pulling the original instance and its positive instance together. To refine the cluster probability distributions of these instances, we introduce a label calibration strategy to model the constraint relationship between instances. Experimental results on two public datasets demonstrate that our proposed method can significantly outperform the previous state-of-the-art methods.- Anthology ID:
- 2022.findings-naacl.186
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2433–2438
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.186
- DOI:
- 10.18653/v1/2022.findings-naacl.186
- Cite (ACL):
- Shusen Wang, Bin Duan, Yanan Wu, and Yajing Xu. 2022. Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2433–2438, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration (Wang et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2022.findings-naacl.186.pdf
- Code
- shusenwang/naacl2022-irlc
- Data
- T-REx