Cross-lingual transfer is essential for building NLP systems for low-resource African languages, but practitioners lack reliable methods for selecting source languages. We systematically evaluate five embedding similarity metrics across 816 transfer experiments spanning three NLP tasks, three African-centric multilingual models, and 12 languages from four language families. We find that cosine gap and retrieval-based metrics (P@1, CSLS) reliably predict transfer success ($ρ= 0.4-0.6$), while CKA shows negligible predictive power ($ρ\approx 0.1$). Critically, correlation signs reverse when pooling across models (Simpson's Paradox), so practitioners must validate per-model. Embedding metrics achieve comparable predictive power to URIEL linguistic typology. Our results provide concrete guidance for source language selection and highlight the importance of model-specific analysis.

翻译：跨语言迁移对于构建低资源非洲语言的NLP系统至关重要，但实践者缺乏选择源语言的可靠方法。我们在涵盖三个NLP任务、三个非洲中心多语言模型以及来自四个语系的12种语言的816次迁移实验中，系统评估了五种嵌入相似性度量。研究发现余弦间隙与基于检索的度量（P@1、CSLS）能可靠预测迁移成功率（$ρ= 0.4-0.6$），而CKA则显示出可忽略的预测能力（$ρ\approx 0.1$）。关键的是，当跨模型汇总数据时相关性符号会发生逆转（辛普森悖论），因此实践者必须按模型进行验证。嵌入度量实现了与URIEL语言类型学相当的预测能力。我们的研究结果为源语言选择提供了具体指导，并强调了模型特异性分析的重要性。