Anaphor Assisted Document-Level Relation Extraction
Chonggang Lu, Richong Zhang, Kai Sun, Jaein Kim, Cunwang Zhang, Yongyi Mao
Abstract
Document-level relation extraction (DocRE) involves identifying relations between entities distributed in multiple sentences within a document. Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities. However, there are two drawbacks in existing methods. On one hand, anaphor plays an important role in reasoning to identify relations between entities but is ignored by these methods. On the other hand, these methods achieve cross-sentence entity interactions implicitly by utilizing a document or sentences as intermediate nodes. Such an approach has difficulties in learning fine-grained interactions between entities across different sentences, resulting in sub-optimal performance. To address these issues, we propose an Anaphor-Assisted (AA) framework for DocRE tasks. Experimental results on the widely-used datasets demonstrate that our model achieves a new state-of-the-art performance.- Anthology ID:
- 2023.emnlp-main.955
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 15453–15464
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.955
- DOI:
- 10.18653/v1/2023.emnlp-main.955
- Cite (ACL):
- Chonggang Lu, Richong Zhang, Kai Sun, Jaein Kim, Cunwang Zhang, and Yongyi Mao. 2023. Anaphor Assisted Document-Level Relation Extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15453–15464, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Anaphor Assisted Document-Level Relation Extraction (Lu et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.emnlp-main.955.pdf