OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres

ACL 2021  ·  YIlun Zhu, Sameer Pradhan, Amir Zeldes ·

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.

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Datasets


Introduced in the Paper:

OntoGUM

Used in the Paper:

GAP Coreference Dataset WikiCoref GUM

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Coreference Resolution OntoGUM SpanBERT Avg F1 64.6 # 1

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