Developing an English-Efik Corpus and Machine Translation System for Digitization Inclusion
It addresses the problem of digitization inclusion for speakers of low-resource languages like Efik, though it is incremental as it applies existing methods to new data.
This study tackled the underrepresentation of the low-resource language Efik in machine translation by evaluating state-of-the-art multilingual models on a small-scale parallel corpus, with NLLB-200 achieving BLEU scores of 26.64 for English-Efik and 31.21 for Efik-English.
Low-resource languages serve as invaluable repositories of human history, preserving cultural and intellectual diversity. Despite their significance, they remain largely absent from modern natural language processing systems. While progress has been made for widely spoken African languages such as Swahili, Yoruba, and Amharic, smaller indigenous languages like Efik continue to be underrepresented in machine translation research. This study evaluates the effectiveness of state-of-the-art multilingual neural machine translation models for English-Efik translation, leveraging a small-scale, community-curated parallel corpus of 13,865 sentence pairs. We fine-tuned both the mT5 multilingual model and the NLLB200 model on this dataset. NLLB-200 outperformed mT5, achieving BLEU scores of 26.64 for English-Efik and 31.21 for Efik-English, with corresponding chrF scores of 51.04 and 47.92, indicating improved fluency and semantic fidelity. Our findings demonstrate the feasibility of developing practical machine translation tools for low-resource languages and highlight the importance of inclusive data practices and culturally grounded evaluation in advancing equitable NLP.