Abstract
The state-of-the-art models for coreference resolution are based on independent mention pair-wise decisions. We propose a modelling approach that learns coreference at the document-level and takes global decisions. For this purpose, we model coreference links in a graph structure where the nodes are tokens in the text, and the edges represent the relationship between them. Our model predicts the graph in a non-autoregressive manner, then iteratively refines it based on previous predictions, allowing global dependencies between decisions. The experimental results show improvements over various baselines, reinforcing the hypothesis that document-level information improves conference resolution.- Anthology ID:
- 2022.findings-acl.215
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2022
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2732–2742
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.findings-acl.215/
- DOI:
- 10.18653/v1/2022.findings-acl.215
- Cite (ACL):
- Lesly Miculicich and James Henderson. 2022. Graph Refinement for Coreference Resolution. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2732–2742, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Graph Refinement for Coreference Resolution (Miculicich & Henderson, Findings 2022)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.findings-acl.215.pdf
- Data
- OntoNotes 5.0