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.
@article{arxiv.2109.09667,
title = {On Generalization in Coreference Resolution},
author = {Shubham Toshniwal and Patrick Xia and Sam Wiseman and Karen Livescu and Kevin Gimpel},
journal= {arXiv preprint arXiv:2109.09667},
year = {2021}
}