  A long-standing challenge in coreference resolution has been the
incorporation of entity-level information - features defined over clusters of
mentions instead of mention pairs. We present a neural network based
coreference system that produces high-dimensional vector representations for
pairs of coreference clusters. Using these representations, our system learns
when combining clusters is desirable. We train the system with a
learning-to-search algorithm that teaches it which local decisions (cluster
merges) will lead to a high-scoring final coreference partition. The system
substantially outperforms the current state-of-the-art on the English and
Chinese portions of the CoNLL 2012 Shared Task dataset despite using few
hand-engineered features.
