A Transformer-Based Yoruba to English Machine Translation (TYEMT) System with Rouge Score

Automated translation systems for some indigenous Nigerian languages like the Yoruba, have historically been limited by the lack of large, high- quality bilingual text and effective approaches to modeling. This paper presents introduces an approach to bi-directional Yoruba-English text-to-text machine translation utilizing deep learning technique, specifically Transformer models. Transformer models, which utilizes self-attention mechanisms to improve translation quality and efficiency. The system was trained and evaluated on a newly curated Yoruba- English parallel corpus, which significantly augments existing resources. Experimental results demonstrate that the Transformer-based model performs translation accurately and fluently, achieving a ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score improvement of 0.4649. This work not only advances the frontiers of Yoruba-English machine translation but also enriches a wider domain in the field of multilingual Natural Language processing (NLP) by addressing challenges associated with translating between languages with limited resources. Future studies include enhancing the available parallel corpus and exploring hybrid models that combine the strengths of both RNN and Transformer architectures.