Adapting Coreference Resolution for Processing Violent Death Narratives
Ankith Uppunda, Susan Cochran, Jacob Foster, Alina Arseniev-Koehler, Vickie Mays, Kai-Wei Chang
Abstract
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.
BibTeX
@inproceedings{uppunda-etal-2021-adapting,
title = "Adapting Coreference Resolution for Processing Violent Death Narratives",
author = "Uppunda, Ankith and
Cochran, Susan and
Foster, Jacob and
Arseniev-Koehler, Alina and
Mays, Vickie and
Chang, Kai-Wei",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.361/",
doi = "10.18653/v1/2021.naacl-main.361",
pages = "4553--4559"
}