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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. This paper provides a dataset and comprehensive evaluation showing thatthe latest neural LM based end-to-end systems degrade very substantially out ofdomain. We make an OntoNotes-like coreference dataset called OntoGUM publiclyavailable, converted from GUM, an English corpus covering 12 genres, usingdeterministic rules, which we evaluate. Thanks to the rich syntactic anddiscourse annotations in GUM, we are able to create the largest human-annotatedcoreference corpus following the OntoNotes guidelines, and the first to beevaluated for consistency with the OntoNotes scheme. Out-of-domain evaluationacross 12 genres shows nearly 15-20% degradation for both deterministic anddeep learning systems, indicating a lack of generalizability or covertoverfitting in existing coreference resolution models.

OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres