Cross-lingual NER Transfer Efficiency with Budget-Xfer: Source Language Embedding Model Comparisons

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 choice of source language embedding models (e.g., multilingual BERT vs. language-specific models) affect the efficiency and accuracy of Budget-Xfer in cross-lingual NER transfer, as evaluated by F1 score and training time on low-resource African languages? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.5/10.