HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid
Hemank Lamba, Anton Abilov, Ke Zhang, Elizabeth M Olson, Henry Kudzanai Dambanemuya, João Cordovil Bárcia, David S. Batista, Christina Wille
Abstract
Humanitarian organizations can enhance their effectiveness by analyzing data to discover trends, gather aggregated insights, manage their security risks, support decision-making, and inform advocacy and funding proposals. However, data about violent incidents with direct impact and relevance for humanitarian aid operations is not readily available. An automatic data collection and NLP-backed classification framework aligned with humanitarian perspectives can help bridge this gap. In this paper, we present HumVI – a dataset comprising news articles in three languages (English, French, Arabic) containing instances of different types of violent incidents categorized by the humanitarian sector they impact, e.g., aid security, education, food security, health, and protection. Reliable labels were obtained for the dataset by partnering with a data-backed humanitarian organization, Insecurity Insight. We provide multiple benchmarks for the dataset, employing various deep learning architectures and techniques, including data augmentation and mask loss, to address different task-related challenges, e.g., domain expansion. The dataset is publicly available at https://github.com/dataminr-ai/humvi-dataset.
BibTeX
@inproceedings{lamba-etal-2024-humvi,
title = "{H}um{VI}: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid",
author = "Lamba, Hemank and
Abilov, Anton and
Zhang, Ke and
Olson, Elizabeth M and
Dambanemuya, Henry Kudzanai and
B{\'a}rcia, Jo{\~a}o Cordovil and
Batista, David S. and
Wille, Christina and
Cahill, Aoife and
Tetreault, Joel R. and
Jaimes, Alejandro",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.743/",
doi = "10.18653/v1/2024.findings-emnlp.743",
pages = "12705--12722"
}