GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Languages

TL;DR

The paper introduces a comprehensive set of 41,513 parallel sentence pairs for five Ghanaian languages, supporting NLP research and applications to promote language preservation and inclusivity.

Contribution

It presents a new, curated multilingual dataset for underrepresented Ghanaian languages, including methodology, structure, and real-world deployment details.

Findings

Created a large, high-quality parallel corpus for five Ghanaian languages.

Enabled improved machine translation and language technology applications.

Facilitated language preservation and digital inclusion efforts.

Abstract

Low resource languages present unique challenges for natural language processing due to the limited availability of digitized and well structured linguistic data. To address this gap, the GhanaNLP initiative has developed and curated 41,513 parallel sentence pairs for the Twi, Fante, Ewe, Ga, and Kusaal languages, which are widely spoken across Ghana yet remain underrepresented in digital spaces. Each dataset consists of carefully aligned sentence pairs between a local language and English. The data were collected, translated, and annotated by human professionals and enriched with standard structural metadata to ensure consistency and usability. These corpora are designed to support research, educational, and commercial applications, including machine translation, speech technologies, and language preservation. This paper documents the dataset creation methodology, structure, intended use cases, and evaluation, as well as their deployment in real world applications such as the Khaya AI translation engine. Overall, this work contributes to broader efforts to democratize AI by enabling inclusive and accessible language technologies for African languages.