In Africa, and the world at large, there is an increasing focus on developing
Neural Machine Translation (NMT) systems to overcome language barriers. NMT for
Low-resource language is particularly compelling as it involves learning with
limited labelled data. However, obtaining a well-aligned parallel corpus for
low-resource languages can be challenging. The disparity between the
technological advancement of a few global languages and the lack of research on
NMT for local languages in Chad is striking. End-to-end NMT trials on
low-resource Chad languages have not been attempted. Additionally, there is a
dearth of online and well-structured data gathering for research in Natural
Language Processing, unlike some African languages. However, a guided approach
for data gathering can produce bitext data for many Chadian language
translation pairs with well-known languages that have ample data. In this
project, we created the first sba-Fr Dataset, which is a corpus of
Ngambay-to-French translations, and fine-tuned three pre-trained models using
this dataset. Our experiments show that the M2M100 model outperforms other
models with high BLEU scores on both original and original+synthetic data. The
publicly available bitext dataset can be used for research purposes.

该研究聚焦于低资源语言，特别关注尼日尔的低资源语言，制定了有效的数据收集方法，构建了第一个 sba-Fr 数据集，并对三个预训练模型进行了优化，结果显示 M2M100 模型在原始数据和原始 + 合成数据上都取得了较高 BLEU 分数，该公开数据集可用于研究目的。