Building a Transformer-Based Neural Machine Translation System for English–Kibajuni Translation: A Low-Resource Deep Learning Approach for Indigenous Language Preservation

Recent progress in artificial intelligence has pushed machine translation to high levels of accuracy for widely resourced languages. Yet for many indigenous and endangered languages, comparable tools remain absent, largely because digitized linguistic data are scarce. Kibajuni, a minimally documented Bantu language spoken along the Kenyan coast, illustrates this gap. Publicly accessible English–Kibajuni machine translation systems are not available, which restricts both everyday digital use and broader language preservation work. This paper reports the design, construction, and assessment of a compact Transformer-based Neural Machine Translation (NMT) system for English–Kibajuni translation. Training relied on a community-produced parallel corpus of roughly 10,000 aligned sentence pairs. A Design Science Research approach guided development of the full translation workflow, beginning with corpus preparation and continuing through Byte Pair Encoding (BPE) tokenization, a custom encoder–decoder Transformer, supervised training in PyTorch, beam-search decoding at inference time, and deployment as a web application. Because data were limited, emphasis was placed on training stability and generalization rather than increasing model size. The system therefore integrated AdamW, OneCycle learning-rate scheduling, dropout, label smoothing, gradient clipping, mixed-precision training, and early stopping driven by validation BLEU. Results indicate that, despite the small dataset, the model learned usable semantic correspondences between English and Kibajuni while remaining computationally light. The final network contains about 6–8 million parameters, occupies roughly 27 MB, and supports real-time translation on modest hardware. In practical terms, the work provides one of the earliest operational English–Kibajuni neural translation platforms. At the methodological level, it offers a reproducible template for developing MT systems for other under-resourced African languages. Taken together, the findings suggest that appropriately scaled Transformer models, paired with subword tokenization and carefully tuned training procedures, can materially advance digital inclusion and language preservation for endangered languages.