Papers › Graph Refinement for Coreference Resolution
Graph Refinement for Coreference Resolution
Lesly Miculicich, James Henderson
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Coreference Resolution | OntoNotes | G2GT SpanBERT-large reduced | F1 | 80.5 | #9 of 26 | Archive leaderboard | report |
| Coreference Resolution | OntoNotes | G2GT SpanBERT-large overlap | F1 | 80.2 | #10 of 26 | Archive leaderboard | report |
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