A New Task and Dataset on Detecting Attacks on Human Rights Defenders
Shihao Ran, Di Lu, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
Abstract
The ability to conduct retrospective analyses of attacks on human rights defenders over time and by location is important for humanitarian organizations to better understand historical or ongoing human rights violations and thus better manage the global impact of such events. We hypothesize that NLP can support such efforts by quickly processing large collections of news articles to detect and summarize the characteristics of attacks on human rights defenders. To that end, we propose a new dataset for detecting Attacks on Human Rights Defenders (HRDsAttack) consisting of crowdsourced annotations on 500 online news articles. The annotations include fine-grained information about the type and location of the attacks, as well as information about the victim(s). We demonstrate the usefulness of the dataset by using it to train and evaluate baseline models on several sub-tasks to predict the annotated characteristics.
BibTeX
@inproceedings{ran-etal-2023-new,
title = "A New Task and Dataset on Detecting Attacks on Human Rights Defenders",
author = "Ran, Shihao and
Lu, Di and
Cahill, Aoife and
Tetreault, Joel and
Jaimes, Alejandro",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.443/",
doi = "10.18653/v1/2023.findings-acl.443",
pages = "7089--7113"
}