Coreference resolution is an intermediate step for text understanding. It is
used in tasks and domains for which we do not necessarily have coreference
annotated corpora. Therefore, generalization is of special importance for
coreference resolution. However, while recent coreference resolvers have
notable improvements on the CoNLL dataset, they struggle to generalize properly
to new domains or datasets. In this paper, we investigate the role of
linguistic features in building more generalizable coreference resolvers. We
show that generalization improves only slightly by merely using a set of
additional linguistic features. However, employing features and subsets of
their values that are informative for coreference resolution, considerably
improves generalization. Thanks to better generalization, our system achieves
state-of-the-art results in out-of-domain evaluations, e.g., on WikiCoref, our
system, which is trained on CoNLL, achieves on-par performance with a system
designed for this dataset.

本研究探讨在核指消解中使用语言特征以提高泛化性能，结果表明，仅增加一组附加的语言特征只略微提高泛化性能，而使用信息量高的特征子集，可以显著提高泛化性能，从而使得我们的系统在 WikiCoref 数据集上获得了与该数据集特定模型相当的表现，实现了最先进的结果。