Ngambay-French Neural Machine Translation (sba-Fr)
Sakayo Toadoum Sari Angela Fan Lema Logamou Seknewna
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.