OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres
This addresses the issue of overfitting and lack of generalizability in coreference resolution for NLP researchers, though it is incremental as it focuses on evaluation rather than new methods.
The paper tackled the problem of evaluating the generalizability of state-of-the-art coreference resolution models beyond the OntoNotes benchmark by creating OntoGUM, a new dataset covering 12 genres, and found that these models degrade by nearly 15-20% out-of-domain.
SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it difficult to evaluate generalizability to open domain data. This paper provides a dataset and comprehensive evaluation showing that the latest neural LM based end-to-end systems degrade very substantially out of domain. We make an OntoNotes-like coreference dataset called OntoGUM publicly available, converted from GUM, an English corpus covering 12 genres, using deterministic rules, which we evaluate. Thanks to the rich syntactic and discourse annotations in GUM, we are able to create the largest human-annotated coreference corpus following the OntoNotes guidelines, and the first to be evaluated for consistency with the OntoNotes scheme. Out-of-domain evaluation across 12 genres shows nearly 15-20% degradation for both deterministic and deep learning systems, indicating a lack of generalizability or covert overfitting in existing coreference resolution models.