The Ghanaian NLP Landscape: A First Look

TL;DR

This paper provides the first comprehensive survey of NLP research on Ghanaian languages, highlighting current methodologies, datasets, challenges, and future directions to promote linguistic diversity in AI.

Contribution

It offers a foundational resource and roadmap for Ghanaian NLP research, addressing the gap in AI support for underrepresented African languages.

Findings

Identified key datasets and methodologies used in Ghanaian NLP

Outlined major challenges and gaps in current research

Proposed future directions to enhance NLP for Ghanaian languages

Abstract

Despite comprising one-third of global languages, African languages are critically underrepresented in Artificial Intelligence (AI), threatening linguistic diversity and cultural heritage. Ghanaian languages, in particular, face an alarming decline, with documented extinction and several at risk. This study pioneers a comprehensive survey of Natural Language Processing (NLP) research focused on Ghanaian languages, identifying methodologies, datasets, and techniques employed. Additionally, we create a detailed roadmap outlining challenges, best practices, and future directions, aiming to improve accessibility for researchers. This work serves as a foundational resource for Ghanaian NLP research and underscores the critical need for integrating global linguistic diversity into AI development.