Improving Span Representation for Domain-adapted Coreference Resolution
Nupoor Gandhi, Anjalie Field, Yulia Tsvetkov · 2021
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 knowledgebased 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 endto-end coreference models results in better performance on the most challenging, domainspecific spans 1 .