Neural Machine Translation in Low-Resourced Languages: Case of Dholuo-Swahili Translation

This study developed a Neural Machine Translation (NMT) model for Dholuo-Swahili translation, addressing the lack of translation resources for lowresource languages in Kenya. Using the OpenNMT-py framework, the model was trained on a parallel dataset of 27,415 sentence pairs from two datasets to improve model performance. Performance was evaluated using BLEU (Bilingual Evaluation Understudy), METEOR (Metric for Evaluation of Translation with Explicit Ordering), perplexity (PPL), and prediction scores, with results showing a BLEU score of 16.29, METEOR score of 0.27, and PPL of 1.88, indicating moderate translation accuracy. Despite achieving low PPL, fluency and lexical accuracy challenges persisted due to limited training data and linguistic differences. Future research will focus on expanding datasets, leveraging multilingual models like LLaMA 3.2, and integrating hybrid approaches to improve translation quality. This work contributes to digital inclusion, language preservation, and enhanced crosslinguistic communication for underrepresented languages in Kenya.