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Despite significant advances in machine translation over the past decade, domain-specific terminology remains a persistent challenge for low-resource African languages. Recent evaluations of neural models indicate that performance drops by as much as 40% when moving from general-domain benchmarks to specialised legal or medical corpora (Adesina & Okonkwo, 2024). This gap is particularly pronounced for languages such as Yoruba, Hausa, and Igbo, where parallel corpora remain scarce and inconsistently annotated. The present study addresses this limitation by constructing a curated bilingual corpus of 50,000 domain-labelled sentence pairs, annotated through a collaborative framework involving native-speaker linguists and domain experts (Ogunleye et al., 2023).
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