Abstract
Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities. However, effective aggregation of relevant information in the document remains a challenging research question. Existing approaches construct static document-level graphs based on syntactic trees, co-references or heuristics from the unstructured text to model the dependencies. Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. We further develop a refinement strategy, which enables the model to incrementally aggregate relevant information for multi-hop reasoning. Specifically, our model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results, and also yields new state-of-the-art results on the CDR and GDA dataset. Furthermore, extensive analyses show that the model is able to discover more accurate inter-sentence relations.- Anthology ID:
- 2020.acl-main.141
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1546–1557
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.141
- DOI:
- 10.18653/v1/2020.acl-main.141
- Cite (ACL):
- Guoshun Nan, Zhijiang Guo, Ivan Sekulic, and Wei Lu. 2020. Reasoning with Latent Structure Refinement for Document-Level Relation Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1546–1557, Online. Association for Computational Linguistics.
- Cite (Informal):
- Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (Nan et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.acl-main.141.pdf
- Code
- nanguoshun/LSR + additional community code
- Data
- CDR, DocRED, GDA