Coreference Resolution With Entity Equalization

A key challenge in coreference resolution is to capture properties of entity clusters, and use those in the resolution process. Here we provide a simple and effective approach for achieving this, via an "Entity Equalization" mechanism. The Equalization approach represents each mention in a cluster via an approximation of the sum of all mentions in the cluster. We show how this can be done in a fully differentiable end-to-end manner, thus enabling high-order inferences in the resolution process. Our approach, which also employs BERT embeddings, results in new state-of-the-art results on the CoNLL-2012 coreference resolution task, improving average F1 by 3.6%.(1)