  Training coreference resolution models require comprehensively labeled data.
A model trained on one dataset may not successfully transfer to new domains.
This paper investigates an approach to active learning for coreference
resolution that feeds discrete annotations to an incremental clustering model.
The recent developments in incremental coreference resolution allow for a novel
approach to active learning in this setting. Through this new framework, we
analyze important factors in data acquisition, like sources of model
uncertainty and balancing reading and labeling costs. We explore different
settings through simulated labeling with gold data. By lowering the data
barrier for coreference, coreference resolvers can rapidly adapt to a series of
previously unconsidered domains.
