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

Abstract

Abstract: 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 Question

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?

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