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A Bi-lingual chatbot implementation for pandemic response using the transformer-based approach

Mugume Twinamatsiko Atwine, Daudi Jjingo*, Mike Nsubuga, Richard Serunjogi, Ibrahim Mbabaali, Ibra Lujumba, Byansi David, Timothy Kintu, Ronald Galiwango, Kebirungi Grace

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

The COVID-19 pandemic highlighted the critical need for timely and accurate information in effectively managing pandemics. The proliferation of misinformation on public media platforms complicated the management of the pandemic, necessitating a large and constant human resource to meet the demand for trustworthy information. To address this challenge, we developed an intelligent bilingual chatbot that is available 24/7 to provide medically curated up-to-date and approved pandemic management information in English and Luganda. This approach leveraged deep learning to train a chatbot on a growing corpus of curated pandemic-specific information, questions, and answers. Our results demonstrate an implementation of a chatbot that leverages the well-resourced English NLP framework to enable chatting in the Luganda language. They also show that the chatbot is an effective and flexible tool for disseminating accurate information in real-time, while also providing opportunities for continuous improvement through conversation-driven development.
Original languageEnglish
Article numbere0001256
Number of pages19
JournalPLOS Digital Health
Volume5
Issue number4
DOIs
Publication statusPublished - 1 Apr 2026

Bibliographical note

Publisher Copyright:
© 2026 Atwine et al.

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