@inproceedings{sakayo-etal-2023-ngambay,
    title = "{N}gambay-{F}rench Neural Machine Translation (sba-Fr)",
    author = "Sakayo, Toadoum Sari  and
      Fan, Angela  and
      Seknewna, Lema Logamou",
    editor = "Guti{\'e}rrez, Raquel L{\'a}zaro  and
      Pareja, Antonio  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the First Workshop on NLP Tools and Resources for Translation and Interpreting Applications",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.nlp4tia-1.6/",
    pages = "39--47",
    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."
}