Vickie Mays


2021

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Adapting Coreference Resolution for Processing Violent Death Narratives
Ankith Uppunda | Susan Cochran | Jacob Foster | Alina Arseniev-Koehler | Vickie Mays | Kai-Wei Chang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Coreference resolution is an important compo-nent in analyzing narrative text from admin-istrative data (e.g., clinical or police sources).However, existing coreference models trainedon general language corpora suffer from poortransferability due to domain gaps, especiallywhen they are applied to gender-inclusive datawith lesbian, gay, bisexual, and transgender(LGBT) individuals. In this paper, we an-alyzed the challenges of coreference resolu-tion in an exemplary form of administrativetext written in English: violent death nar-ratives from the USA’s Centers for DiseaseControl’s (CDC) National Violent Death Re-porting System. We developed a set of dataaugmentation rules to improve model perfor-mance using a probabilistic data programmingframework. Experiments on narratives froman administrative database, as well as existinggender-inclusive coreference datasets, demon-strate the effectiveness of data augmentationin training coreference models that can betterhandle text data about LGBT individuals.