Abstract
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