Academic Research

We contribute to peer-reviewed publications, conferences, and workshops. Our academic work is focused on advancing the global understanding of low-resource language modelling, culturally-aware AI, and multimodal learning architectures.

How Effective Are AI Models in Translating English Scientific Texts to Pidgin: A Low-Resource Language?

This research explores the challenges and limitations of applying deep learning models to the translation of scientific texts from English to Nigerian Pidgin, a widely spoken but low-resource language in West Africa. Despite advancements in machine translation, translating domain-specific content such as biological research papers presents unique obstacles, including data scarcity, linguistic complexity, and model generalization issues.

How Effective Are AI Models in Translating English Scientific Texts to Pidgin: A Low-Resource Language?

Machine translation (MT) has made significant progress in high-resource languages, but translating technical texts into low-resource languages remains an open challenge. This study investigates the ability of state-of-the-art multilingual models to translate animal health reports from English to Yoruba, a crucial task for enhancing veterinary communication in underserved regions. Although previous research has explored low-resource MT, domain-specific translation for animal health has been largely overlooked.