  Academic neural models for coreference resolution (coref) are typically
trained on a single dataset, OntoNotes, and model improvements are benchmarked
on that same dataset. However, real-world applications of coref 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 coref models
based on the number of annotated documents available in the target dataset. We
examine eleven 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 PreCo.
