Research Article
Neural Machine Translation for Mooré, a Low-Resource Language
@INPROCEEDINGS{10.4108/eai.18-12-2023.2348140, author={Hamed Joseph Ouily and Aminata Sabane and Delwende Eliane Birba and Rodrique Kafando and Abdoul Kader Kabore and Tegawende F. Bissyand\^{e}}, title={Neural Machine Translation for Moor\^{e}, a Low-Resource Language}, proceedings={Proceedings of the 6th Computer Science Research Days, JRI 2023, 18-20 December 2023, Ouagadougou, Burkina Faso}, publisher={EAI}, proceedings_a={JRI}, year={2024}, month={6}, keywords={natural language processing neural machine translation low-ressource language local language}, doi={10.4108/eai.18-12-2023.2348140} }- Hamed Joseph Ouily
Aminata Sabane
Delwende Eliane Birba
Rodrique Kafando
Abdoul Kader Kabore
Tegawende F. Bissyandé
Year: 2024
Neural Machine Translation for Mooré, a Low-Resource Language
JRI
EAI
DOI: 10.4108/eai.18-12-2023.2348140
Abstract
Natural Language Processing (NLP) is a field of artificial intelligence with the goal of enabling machines to understand human language. Neural Machine Translation (NMT) is one of the many applications of NLP and allows for the translation of a source language into a target language. NMT has made significant progress in recent years. However, most African languages, especially those in Burkina Faso, have received very little research attention in this context. In this article, we propose automated translation models for Mooré language to French based on Transformers. We obtained an average BLUE score of 44.82 for the model trained on all the data and 65.75 for the model trained only with the Jehovah’s Witnesses Bible data for the machine translation task from Mooré to French. These encouraging results may evolve as the work is still in progress.
