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    <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.</abstract>
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%0 Conference Proceedings
%T A New Task and Dataset on Detecting Attacks on Human Rights Defenders
%A Ran, Shihao
%A Lu, Di
%A Cahill, Aoife
%A Tetreault, Joel
%A Jaimes, Alejandro
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ran-etal-2023-new
%X 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.
%R 10.18653/v1/2023.findings-acl.443
%U https://aclanthology.org/2023.findings-acl.443
%U https://doi.org/10.18653/v1/2023.findings-acl.443
%P 7089-7113

Markdown (Informal)

[A New Task and Dataset on Detecting Attacks on Human Rights Defenders](https://aclanthology.org/2023.findings-acl.443) (Ran et al., Findings 2023)

• A New Task and Dataset on Detecting Attacks on Human Rights Defenders (Ran et al., Findings 2023)

ACL

• Shihao Ran, Di Lu, Aoife Cahill, Joel Tetreault, and Alejandro Jaimes. 2023. A New Task and Dataset on Detecting Attacks on Human Rights Defenders. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7089–7113, Toronto, Canada. Association for Computational Linguistics.