A Congolese Swahili Task-Oriented Dialogue System for Addressing Humanitarian Crises

As Artificial Intelligence (AI) advances, conversational agents are increasingly used across sectors, including humanitarian response. However, current systems and datasets mainly support high-resource languages and open-domain tasks, resulting in significant limitations in addressing low-resource, domain-specific needs. This study addresses this gap by focusing on a Congolese Swahili corpus collected from Short Message Service (SMS) messages and call-center humanitarian questions to develop an effective conversational agent for low-resource languages that supports communication during humanitarian crises. The goal of this research is to develop an effective Task-Oriented Dialogue System (ToDS) to assist displaced persons seeking humanitarian information in Congolese Swahili. We built a pipeline-based ToDS that converts natural language into SPARQL by utilizing a trained Named Entity Recognition (NER) model and a Dual Intent and Entity Transformer (DIET) classifier. This ToDS includes a humanitarian-specific ontology and dynamically queries a local triple store with data derived from the Humanitarian Data Exchange (HDX). The preliminary results indicate high accuracy in entity recognition and intent classification, which enables precise and timely information responses. The agent effectively provides context-relevant answers to humanitarian questions in crisis interactions. The findings demonstrate that applying Natural Language Understanding (NLU) methods in a low-resource, crisis-based context is viable and impactful. This ToDS offers a scalable solution for improving information accessibility in humanitarian emergencies and during forced internal displacements.