Abstract
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 stateof-the-art results on the CoNLL-2012 coreference resolution task, improving average F1 by
3.6%