Authors : Emmanuel Makoji ; Felix Sani

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/yckar8pn

DOI : https://doi.org/10.38124/ijisrt/25may556

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Low-resource languages face significant challenges in the digital age due to limited computational tools and data resources. This study presents the development of a neural machine translation (NMT) system for English-to-Igala translation using a Recurrent Neural Network (RNN) model. Igala is one of the under-resourced languages spoken in Nigeria. A bilingual parallel corpus of 1000 English-Igala sentence pairs was compiled and preprocessed to train and evaluate the system. The model achieved high translation accuracy as evidenced by BLEU scores above 0.5 on most test sentences. This research provides a foundational step for the development of computational resources for Igala and supports the broader goal of linguistic inclusivity in artificial intelligence.

Keywords : Neural Machine Translation, Low-Resource Languages, Igala, Deep Learning, Recurrent Neural Network, BLEU Score.

References :

1. Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. International Conference on Learning Representations (ICLR).
2. Currey, A., Michel, J., & Heafield, K. (2017). Copied monolingual data improves low-resource neural machine translation. WMT 2017.
3. Koehn, P., & Knowles, R. (2017). Six challenges for neural machine translation. Proceedings of the First Workshop on Neural Machine Translation, 28–39.
4. Lample, G., Conneau, A., Denoyer, L., & Ranzato, M. (2018). Unsupervised machine translation using monolingual corpora only. International Conference on Learning Representations (ICLR).
5. Oyewole, A., & Iwu, C. G. (2020). The state of machine translation for Nigerian languages. African Journal of Computing and ICT, 13(1), 1–12.
6. Sani, A. (2016). Development of a rule-based machine translation system for English to Igala. Journal of African Language Technology, 4(2), 45–59.
7. Sani, A. (2023). The digital divide and the role of translation in Africa. Language and Society in Africa, 12(3), 101–117.
8. Sennrich, R., Haddow, B., & Birch, A. (2016). Improving neural machine translation models with monolingual data. ACL 2016, 86–96.
9. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.