  While coreference resolution is defined independently of dataset domain, most
models for performing coreference resolution do not transfer well to unseen
domains. We consolidate a set of 8 coreference resolution datasets targeting
different domains to evaluate the off-the-shelf performance of models. We then
mix three datasets for training; even though their domain, annotation
guidelines, and metadata differ, we propose a method for jointly training a
single model on this heterogeneous data mixture by using data augmentation to
account for annotation differences and sampling to balance the data quantities.
We find that in a zero-shot setting, models trained on a single dataset
transfer poorly while joint training yields improved overall performance,
leading to better generalization in coreference resolution models. This work
contributes a new benchmark for robust coreference resolution and multiple new
state-of-the-art results.
