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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

TaskDatasetResultRank
Coreference ResolutionGAP (test)
Overall F189.6
53
Coreference ResolutionOntoNotes
MUC86.6
23
Coreference ResolutionEnglish OntoNotes 5.0 (test)
MUC Precision88.1
18
Coreference ResolutionWikiCoref (WC) (test)
Average F162.6
12
Coreference ResolutionWinogender (WG) (test)
Accuracy77.3
11
Coreference ResolutionWinoBias (test)
Accuracy85.1
2
Coreference ResolutionBUG (test)
Accuracy74.6
2
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