  Academic neural models for coreference resolution are typically trained on a
single dataset (OntoNotes) and model improvements are then benchmarked on that
dataset. However, real-world usages of coreference resolution models depend on
the annotation guidelines and the domain of the target dataset, which often
differ from those of OntoNotes. We aim to quantify transferability of
coreference resolution models based on the number of annotated documents
available in the target dataset. We examine five target datasets and find that
continued training is consistently effective and especially beneficial when
there are few target documents. We establish new benchmarks across several
datasets, including state-of-the-art results on LitBank and PreCo.
