Ngambay-French Neural Machine Translation (sba-Fr)
- URL: http://arxiv.org/abs/2308.13497v1
- Date: Fri, 25 Aug 2023 17:13:20 GMT
- Title: Ngambay-French Neural Machine Translation (sba-Fr)
- Authors: Sakayo Toadoum Sari and Angela Fan and Lema Logamou Seknewna
- Abstract summary: In Africa, and the world at large, there is an increasing focus on developing Neural Machine Translation (NMT) systems to overcome language barriers.
In this project, we created the first sba-Fr dataset, which is a corpus of Ngambay-to-French translations.
Our experiments show that the M2M100 model outperforms other models with high BLEU scores on both original and original+synthetic data.
- Score: 16.55378462843573
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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