Building a Functional Machine Translation Corpus for Kpelle

Kweku Andoh Yamoah, Jackson Weako, Emmanuel J. Dorley
5/24/2025
cs.CL

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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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Cite this paper

@article{yamoah2025building,
  title  = {Building a Functional Machine Translation Corpus for Kpelle},
  author = {Kweku Andoh Yamoah and Jackson Weako and Emmanuel J. Dorley},
  year   = {2025},
  eprint = {2505.18905},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2505.18905v1}
}

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