  There is compelling evidence that coreference prediction would benefit from
modeling global information about entity-clusters. Yet, state-of-the-art
performance can be achieved with systems treating each mention prediction
independently, which we attribute to the inherent difficulty of crafting
informative cluster-level features. We instead propose to use recurrent neural
networks (RNNs) to learn latent, global representations of entity clusters
directly from their mentions. We show that such representations are especially
useful for the prediction of pronominal mentions, and can be incorporated into
an end-to-end coreference system that outperforms the state of the art without
requiring any additional search.
