首个公开的英语-克佩勒语翻译数据集，助力低资源语言技术发展。
Building a Functional Machine Translation Corpus for Kpelle
- 构建2000+句对的英克佩勒语平行语料库，覆盖日常、宗教与教育文本。
- 微调NLLB模型后，克佩勒语转英语方向达30分BLEU，验证数据增强有效性。
- 推动语音识别与语言建模等任务，适合低资源语言研究者参考。
本文首次发布公开可用的英语-克佩勒语机器翻译数据集，包含超过2000个句子对，源自日常交流、宗教文本和教育材料。通过在该数据集两个版本上微调Meta的No Language Left Behind (NLLB) 模型，克佩勒语到英语方向最高获得30分的BLEU分数，证明了数据增强的有效性。研究结果与NLLB-200在其他非洲语言上的基准表现一致，表明尽管克佩勒语为低资源语言，仍具备竞争性性能潜力。该数据集不仅支持机器翻译，还可用于语音识别与语言建模等更广泛的自然语言处理任务。文章最后提出未来扩展路线图，强调拼写一致性、社区参与验证及跨学科合作，以推动克佩勒语及其他低资源曼德语族语言的技术包容性发展。
原文摘要 · Abstract (English)
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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