  Resolving pronoun coreference requires knowledge support, especially for
particular domains (e.g., medicine). In this paper, we explore how to leverage
different types of knowledge to better resolve pronoun coreference with a
neural model. To ensure the generalization ability of our model, we directly
incorporate knowledge in the format of triplets, which is the most common
format of modern knowledge graphs, instead of encoding it with features or
rules as that in conventional approaches. Moreover, since not all knowledge is
helpful in certain contexts, to selectively use them, we propose a knowledge
attention module, which learns to select and use informative knowledge based on
contexts, to enhance our model. Experimental results on two datasets from
different domains prove the validity and effectiveness of our model, where it
outperforms state-of-the-art baselines by a large margin. Moreover, since our
model learns to use external knowledge rather than only fitting the training
data, it also demonstrates superior performance to baselines in the
cross-domain setting.

Computation and Language