Adapting Coreference Resolution for Processing Violent Death Narratives

By Ankith Uppunda, Susan D. Cochran, Jacob Gates Foster, Alina Arseniev-Koehler, Vickie M. Mays, Kai-Wei Chang. 2021

9 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, General Social Sciences, Social Psychology, Natural Language Processing

Citations in the corpus

• Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies (2021, neutral): “Current language models can perpetrate harms such as the cyclical erasure of non-binary gender identities (Uppunda et al., 2021; Sap, 2021; Lakoff; Fiske, 1993; Fast et al., 2016; Behm-Morawitz and Mastro, 2008).”