Neural Recovery of Historical Lexical Structure in Bantu Languages from Modern Data
2026-04-24 • Machine Learning
Machine LearningComputation and Language
AI summaryⓘ
The authors explored whether modern neural models trained on Bantu language data can find similarities in words that match historical language reconstructions. They used a transformer model on 14 Bantu languages and found many noun and verb groups shared across languages that correspond closely to ancient Proto-Bantu words identified by experts. Testing another translation model confirmed these word groupings also reflect known language family relationships. The study shows that neural models can uncover deep linguistic connections consistent with historical understanding, but only within Eastern and Southern Bantu languages.
Bantu languagesProto-BantuNeural modelsTransformerCognatesMorphological paradigmsLexical reconstructionGuthrie classificationCross-lingual embeddingsNoun classes
Authors
Hillary Mutisya, John Mugane
Abstract
We investigate whether neural models trained exclusively on modern morphological data can recover cross-lingual lexical structure consistent with historical reconstruction. Using BantuMorph v7, a transformer over Bantu morphological paradigms, we analyze 14 Eastern and Southern Bantu languages, extract encoder embeddings for their noun and verb lemmas, and identify 728 noun and 1,525 verb cognate candidates shared across 5+ languages. Evaluating these candidates against established historical resources-the Bantu Lexical Reconstructions database (BLR3; 4,786 reconstructed Proto-Bantu forms) and the ASJP basic vocabulary-we confirm 10 of the top 11 noun candidates (90.9%) align with previously reconstructed Proto-Bantu forms, including *-ntU 'person' (8 languages), *gombe 'cow' (9 languages), and *mUn (9 languages). Extending to verbs, 12 verb cognates align with reconstructed Proto-Bantu roots, including *-bon- 'see' and *-jIm- 'stand', each attested across wide geographic ranges. Cross-model validation using an independent translation model (NLLB-600M) confirms these patterns: both models recover cognate clusters and phylogenetic groupings consistent with established Guthrie-zone classifications (p < 0.01). Cross-lingual noun class analysis reveals that all 13 productive classes maintain >0.83 cosine similarity across languages (within-class > between-class, p < 10^-9). Our dataset is restricted to Eastern and Southern Bantu, so we interpret these results as recovering shared Bantu lexical structure consistent with Proto-Bantu rather than definitively distinguishing Proto-Bantu retentions from later regional innovations.