Performance comparison of cross-lingual transfer learning methods for Named Entity Recognition in low-resource African languages

Cross-lingual transfer learning enables NLP for low-resource languages by leveraging labeled data from higher-resource sources, yet existing comparisons of source language selection strategies do not control for total training data, confounding language selection effects with data quantity effects. We introduce Budget-Xfer, a framework that formulates multi-source cross-lingual transfer as a budget-constrained resource allocation problem. Given a fixed annotation budget B, our framework jointly optimizes which source languages to include and how much data to allocate from each. We evaluate fou Research goal: How does the performance of cross-lingual transfer learning for Named Entity Recognition in low-resource African languages compare when using Budget-Xfer versus traditional single-source or multi-source transfer methods, as measured by F1 scores on the MasakhaNER dataset? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.7/10.