LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution
About
While coreference resolution typically involves various linguistic challenges, recent models are based on a single pairwise scorer for all types of pairs. We present LingMess, a new coreference model that defines different categories of coreference cases and optimize multiple pairwise scorers, where each scorer learns a specific set of linguistic challenges. Our model substantially improves pairwise scores for most categories and outperforms cluster-level performance on Ontonotes and 5 additional datasets. Our model is available in https://github.com/shon-otmazgin/lingmess-coref
Shon Otmazgin, Arie Cattan, Yoav Goldberg• 2022
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Coreference Resolution | GAP (test) | Overall F189.6 | 53 | |
| Coreference Resolution | OntoNotes | MUC86.6 | 23 | |
| Coreference Resolution | English OntoNotes 5.0 (test) | MUC Precision88.1 | 18 | |
| Coreference Resolution | WikiCoref (WC) (test) | Average F162.6 | 12 | |
| Coreference Resolution | Winogender (WG) (test) | Accuracy77.3 | 11 | |
| Coreference Resolution | WinoBias (test) | Accuracy85.1 | 2 | |
| Coreference Resolution | BUG (test) | Accuracy74.6 | 2 |
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