text: 111,176,1443062326295506944,1235230714922184712,Nupoor Gandhi,"Happy to share our new paper: Improving Span Representation for Domain-adapted Coreference Resolution (<LINK>) in the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference #EMNLP2021 ! joint work with @anjalie_f and Yulia Tsvetkov We propose two new losses to incorporate external knowledge for more data-efficient fine-tuning of coreference models.",https://arxiv.org/abs/2109.09811,"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. ",Improving Span Representation for Domain-adapted Coreference Resolution,2,"['Happy to share our new paper:\xa0Improving Span Representation for Domain-adapted Coreference Resolution (<LINK>) in the\xa0Fourth Workshop on Computational Models of Reference, Anaphora and Coreference #EMNLP2021 !\n\njoint work with @anjalie_f and Yulia Tsvetkov', 'We propose two new losses to incorporate external knowledge for more data-efficient fine-tuning of coreference models.']",21,09,374