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arXiv · 2505.18905

Building a Functional Machine Translation Corpus for Kpelle

Kweku Andoh Yamoah·Jackson Weako·Emmanuel J. Dorley
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Abstract

In this paper, we introduce the first publicly available English-Kpelle dataset for machine translation, comprising over 2000 sentence pairs drawn from everyday communication, religious texts, and educational materials. By fine-tuning Meta's No Language Left Behind(NLLB) model on two versions of the dataset, we achieved BLEU scores of up to 30 in the Kpelle-to-English direction, demonstrating the benefits of data augmentation. Our findings align with NLLB-200 benchmarks on other African languages, underscoring Kpelle's potential for competitive performance despite its low-resource status. Beyond machine translation, this dataset enables broader NLP tasks, including speech recognition and language modelling. We conclude with a roadmap for future dataset expansion, emphasizing orthographic consistency, community-driven validation, and interdisciplinary collaboration to advance inclusive language technology development for Kpelle and other low-resourced Mande languages.

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Kweku Andoh Yamoah, Jackson Weako, Emmanuel J. Dorley. 2025-05-24. Building a Functional Machine Translation Corpus for Kpelle. https://doi.org/10.18653/v1%2F2025.africanlp-1.8

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