Ngambay-French Neural Machine Translation (sba-Fr)

TL;DR

This paper introduces the first Ngambay-French translation dataset and fine-tunes models for low-resource NMT, demonstrating the effectiveness of the M2M100 model with high BLEU scores.

Contribution

Created the first Ngambay-French translation dataset and fine-tuned pre-trained models for low-resource NMT in Chad.

Findings

M2M100 outperforms other models in BLEU scores

The dataset enables further research in Ngambay language translation

Synthetic data improves translation performance

Abstract

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.