A New Task and Dataset on Detecting Attacks on Human Rights Defenders
Shihao Ran Di Lu Joel Tetreault Aoife Cahill Alejandro Jaimes
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.