Advancing Low-Resource African Language Technologies: Morphological Feature Integration for Kiswahili Question Answering

Low-resource African languages remain critically underrepresented in natural language processing despite serving hundreds of millions of speakers across diverse linguistic communities. This paper addresses whether explicit morphological feature integration can overcome transformer limitations for Kiswahili, an agglutinative Bantu language spoken by over 100 million people across East and Central Africa. The agglutinative nature of Kiswahili presents fundamental challenges to subword tokenization algorithms that break the grammar and require implicit pattern learning using a small amount of data. The research tested vanilla XLM-RoBERTa on the KenSwQuAD question answering dataset, achieving 20.05% F1 and 17.80% Exact Match on the validation set. This weak baseline performance highlighted the significant limitations of traditional multilingual methods with morphologically complex low-resource languages. The study extended XLM-RoBERTa with explicit representations of 17 Kiswahili morphemes to build a morphologically-enhanced architecture, encoded as multi-hot vectors and introduced through learned projection layers and the pre-trained encoder being frozen to maintain multilingual knowledge. The optimized model's F1 and Exact Match scores of 72.40% and 62.91%, respectively, represented substantial improvements of 52.35 percentage points in F1 and 45.11 percentage points in Exact Match from baseline. Rigorous ablation studies demonstrated that improvements were due to the integration of morphological features, not to model capacity. This work demonstrates that explicit linguistic knowledge integration enables competitive performance even with severely limited training data, providing a reproducible framework for morphologically rich under-resourced African languages and challenging prevailing assumptions about the universal applicability of data-driven approaches.