Typological Features in Source Language Selection for Cross-Lingual NER on Low-Resource African Languages

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

Does incorporating typological features into the selection of multiple source languages reduce the performance degradation of cross-lingual NER models on low-resource African languages compared to random source selection?

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