  We propose a triad-based neural network system that generates affinity scores
between entity mentions for coreference resolution. The system simultaneously
accepts three mentions as input, taking mutual dependency and logical
constraints of all three mentions into account, and thus makes more accurate
predictions than the traditional pairwise approach. Depending on system
choices, the affinity scores can be further used in clustering or mention
ranking. Our experiments show that a standard hierarchical clustering using the
scores produces state-of-art results with gold mentions on the English portion
of CoNLL 2012 Shared Task. The model does not rely on many handcrafted features
and is easy to train and use. The triads can also be easily extended to polyads
of higher orders. To our knowledge, this is the first neural network system to
model mutual dependency of more than two members at mention level.
