Papers › Higher-order Coreference Resolution with Coarse-to-fine Inference
Higher-order Coreference Resolution with Coarse-to-fine Inference
Kenton Lee, Luheng He, Luke Zettlemoyer
We introduce a fully differentiable approximation to higher-order inference for coreference resolution. Our approach uses the antecedent distribution from a span-ranking architecture as an attention mechanism to iteratively refine span representations. This enables the model to softly consider multiple hops in the predicted clusters. To alleviate the computational cost of this iterative process, we introduce a coarse-to-fine approach that incorporates a less accurate but more efficient bilinear factor, enabling more aggressive pruning without hurting accuracy. Compared to the existing state-of-the-art span-ranking approach, our model significantly improves accuracy on the English OntoNotes benchmark, while being far more computationally efficient.
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Code
Syntology Ran 2 of 23 code samples harvested from 3 repositories linked to this paper; 21 have no recorded run. Of those that ran: 2 ran · our draft was wrong.
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Code Syntology ran Syntology
23 samples harvested; 2 ran; 0 honoured the contract we drafted; 21 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Coreference Resolution | CoNLL 2012 | c2f-coref + ELMo | Avg F1 | 73.0 | #15 of 18 | Archive leaderboard | report |
| Coreference Resolution | OntoNotes | c2f-coref | F1 | 73.0 | #19 of 26 | Archive leaderboard | report |
| Coreference Resolution | OntoNotes | e2e-coref + ELMo + hyperparameter tuning | F1 | 72.3 | #20 of 26 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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