Learning Global Features for Coreference Resolution

Understand

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.