CEHA: A Dataset of Conflict Events in the Horn of Africa

TL;DR

This paper introduces CEHA, a new dataset of 500 detailed conflict event descriptions in the Horn of Africa, designed to improve NLP models' ability to classify violent conflict types relevant to humanitarian efforts.

Contribution

The paper presents a novel dataset with fine-grained conflict event labels specific to the Horn of Africa and establishes benchmark tasks for event relevance and type classification.

Findings

Baseline models highlight the dataset's challenge and utility.

The dataset supports evaluation of NLP models in low-resource conflict settings.

Extensive experiments demonstrate the dataset's applicability for conflict analysis.

Abstract

Natural Language Processing (NLP) of news articles can play an important role in understanding the dynamics and causes of violent conflict. Despite the availability of datasets categorizing various conflict events, the existing labels often do not cover all of the fine-grained violent conflict event types relevant to areas like the Horn of Africa. In this paper, we introduce a new benchmark dataset Conflict Events in the Horn of Africa region (CEHA) and propose a new task for identifying violent conflict events using online resources with this dataset. The dataset consists of 500 English event descriptions regarding conflict events in the Horn of Africa region with fine-grained event-type definitions that emphasize the cause of the conflict. This dataset categorizes the key types of conflict risk according to specific areas required by stakeholders in the Humanitarian-Peace-Development Nexus. Additionally, we conduct extensive experiments on two tasks supported by this dataset: Event-relevance Classification and Event-type Classification. Our baseline models demonstrate the challenging nature of these tasks and the usefulness of our dataset for model evaluations in low-resource settings with limited number of training data.