  Recent work has shown fine-tuning neural coreference models can produce
strong performance when adapting to different domains. However, at the same
time, this can require a large amount of annotated target examples. In this
work, we focus on supervised domain adaptation for clinical notes, proposing
the use of concept knowledge to more efficiently adapt coreference models to a
new domain. We develop methods to improve the span representations via (1) a
retrofitting loss to incentivize span representations to satisfy a
knowledge-based distance function and (2) a scaffolding loss to guide the
recovery of knowledge from the span representation. By integrating these
losses, our model is able to improve our baseline precision and F-1 score. In
particular, we show that incorporating knowledge with end-to-end coreference
models results in better performance on the most challenging, domain-specific
spans.
