Papers › End-to-end Neural Coreference Resolution
End-to-end Neural Coreference Resolution
Kenton Lee, Luheng He, Mike Lewis, Luke Zettlemoyer
We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.
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Code
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Code Syntology ran Syntology
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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 | e2e-coref + ELMo | Avg F1 | 70.4 | #16 of 18 | Archive leaderboard | report |
| Coreference Resolution | CoNLL 2012 | e2e-coref (ensemble) | Avg F1 | 68.8 | #17 of 18 | Archive leaderboard | report |
| Coreference Resolution | CoNLL 2012 | e2e-coref (single) | Avg F1 | 67.2 | #18 of 18 | Archive leaderboard | report |
| Coreference Resolution | OntoNotes | e2e-coref | F1 | 67.2 | #23 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.
Methods
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