Development of a Transformer-Based Neural Machine Translation System for English to Ebira Language

Transformer-based neural machine translation (NMT) models have boosted translation accuracy for high-resource languages; however, research has largely bypassed unwritten and low-resource languages, particularly African languages such as Ebira. Ebira is an unwritten, low-resource language spoken by approximately 2.5 million people predominantly in Kogi State, Nigeria. Existing Ebira machine translation (MT) systems suffer from poor fluency, accuracy, and missed nuances, constrained by small datasets and rule-based methods. This study presents the development of a neural machine translation (NMT) system for English-to-Ebira translation using Google’s T5-base transformer model. A bilingual parallel corpus of 32,322 English-Ebira sentence pairs was compiled and used to fine-tune the model. The system achieved a corpus-level BLEU score of 40.95%, with 87% of evaluated sentences scoring 0.5 BLEU or higher, surpassing the prior rule-based system’s threshold result of 81.50%, corresponding to 6.75% relative improvement. Human evaluation by ten native Ebira speakers yielded a mean rating of 8.33/10 for fluency, accuracy, and cultural relevance. This research demonstrated that the application of transfer learning on transformer NMT model significantly improves the quality of (MT) systems; and also provides a foundational step for the development of computational resources for Ebira and supports the broader goal of linguistic inclusivity in artificial intelligence.