  Recent work on entity coreference resolution (CR) follows current trends in
Deep Learning applied to embeddings and relatively simple task-related
features. SOTA models do not make use of hierarchical representations of
discourse structure. In this work, we leverage automatically constructed
discourse parse trees within a neural approach and demonstrate a significant
improvement on two benchmark entity coreference-resolution datasets. We explore
how the impact varies depending upon the type of mention.
