Abstract
Document-level relation extraction (DocRE) aims to extract relations among entities within a document, which is crucial for applications like knowledge graph construction. Existing methods usually assume that entities and their mentions are identified beforehand, which falls short of real-world applications. To overcome this limitation, we propose TaG, a novel table-to-graph generation model for joint extractionof entities and relations at document-level. To enhance the learning of task dependencies, TaG induces a latent graph among mentions, with different types of edges indicating different task information, which is further broadcast with a relational graph convolutional network. To alleviate the error propagation problem, we adapt the hierarchical agglomerative clustering algorithm to back-propagate task information at decoding stage. Experiments on the benchmark dataset, DocRED, demonstrate that TaG surpasses previous methods by a large margin and achieves state-of-the-art results.- Anthology ID:
- 2023.acl-long.607
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10853–10865
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.607
- DOI:
- 10.18653/v1/2023.acl-long.607
- Cite (ACL):
- Ruoyu Zhang, Yanzeng Li, and Lei Zou. 2023. A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10853–10865, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction (Zhang et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.acl-long.607.pdf