Low-Resource Machine Translation of Yoruba Text to Nigerian Pidgin Using NLLB-200 and Parameter-Efficient Fine-Tuning

Abstract

Machine translation for low-resource and structurally divergent languages remains a significant challenge, particularly when mapping highly tonal languages to contact languages with fluid orthographies. This study presents the development of a translation system for Yoruba to Nigerian Pidgin, which addresses a critical gap in African natural language processing. Using a custom-curated parallel corpus of 1,846 pairs, the NLLB-200 (600M) foundational model was adapted for this specific language pair. To reduce the computational cost of full-model fine-tuning, Low-Rank Adaptation (LoRA), a Parameter-Efficient Fine-Tuning (PEFT) technique, was applied to the transformer's attention layers. Applying systematic hyperparameter ablation, the model was evaluated across various LoRA ranks and beam search decoding sizes. The results from the evaluation demonstrate that the optimal configuration (LoRA rank 16, Beam Size 4) significantly outperformed the zero-shot baseline (paired bootstrap resampling, p < 0.001), improving the BLEU (Bilingual Evaluation Understudy) score from 0.74 to 30.97 and the character-level score from 9.59 to 50.63. Further qualitative evaluations confirmed the model's ability to generate semantically adequate and colloquially natural translations despite the lack of a standardized dictionary for Nigerian Pidgin. These findings demonstrate the efficacy of parameter-efficient adaptation strategies in democratizing translation technologies for underrepresented, non-standardized linguistic environments.