A Hybrid mT5-Based Machine Translation System for Kanuri–English Educational Translation in a Low-Resource Setting
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Kanuri is a morphologically rich, low-resource language spoken primarily in northeastern Nigeria and the Lake Chad basin, yet it remains almost entirely absent from modern natural language processing (NLP) and machine translation (MT) research. This paper presents a hybrid AI-based Kanuri–English machine translation system designed for primary school educational use. The proposed system integrates the multilingual Text-to-Text Transfer Transformer (mT5) with a deterministic dictionary-based lookup module to address the critical challenge of data scarcity inherent to low-resource language translation. A domain-specific parallel corpus of 522 bilingual entries—comprising classroom instructions, greetings, and basic educational vocabulary—was manually compiled and used to fine-tune the mT5 model, while a structured Kanuri–English lexicon was developed to supplement neural translation outputs. The hybrid architecture exploits the complementary strengths of learned neural representations and rule-based lexical mappings, substantially improving translation accuracy and semantic reliability within the educational domain. An interactive web-based interface incorporating text input, browser-based speech recognition, automatic translation, and text-to-speech output was implemented to support multimodal engagement for young learners and educators. Evaluation using accuracy, precision, recall, and F1-score demonstrates that the hybrid system achieves 100% accuracy on the in-domain evaluation set, compared with 34.67% for the standalone mT5 model. Error analysis confirms that hybrid integration mitigates the generalization weaknesses of purely neural approaches in low-resource conditions. The findings provide a replicable framework for inclusive NLP tool development for other underrepresented African languages and contribute to the broader goals of educational equity and linguistic inclusion for Kanuri-speaking primary school students.
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