  The state-of-the-art models for coreference resolution are based on
independent mention pair-wise decisions. We propose a modelling approach that
learns coreference at the document-level and takes global decisions. For this
purpose, we model coreference links in a graph structure where the nodes are
tokens in the text, and the edges represent the relationship between them. Our
model predicts the graph in a non-autoregressive manner, then iteratively
refines it based on previous predictions, allowing global dependencies between
decisions. The experimental results show improvements over various baselines,
reinforcing the hypothesis that document-level information improves conference
resolution.
