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SOTA coreference resolution produces increasingly impressive scores on theOntoNotes benchmark. However lack of comparable data following the same schemefor more genres makes it difficult to evaluate generalizability to open domaindata. Zhu et al. (2021) introduced the creation of the OntoGUM corpus forevaluating geralizability of the latest neural LM-based end-to-end systems.This paper covers details of the mapping process which is a set ofdeterministic rules applied to the rich syntactic and discourse annotationsmanually annotated in the GUM corpus. Out-of-domain evaluation across 12 genresshows nearly 15-20% degradation for both deterministic and deep learningsystems, indicating a lack of generalizability or covert overfitting inexisting coreference resolution models.

Anatomy of OntoGUM--Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms