Building a Transformer-Based Neural Machine Translation System for English–Kibajuni Translation: A Low-Resource Deep Learning Approach for Indigenous Language Preservation
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Date
2026-07
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Publisher
South Sahara Academic Publishing South Sahara Multidisciplinary
Abstract
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.
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Keywords
Key Words: Artificial Intelligence, Byte Pair Encoding (BPE), Digital Language Preservation, English–Kibajuni Translation, Indigenous Languages, Low-Resource Neural Machine Translation, Natural Language Processing, Transformer Architecture