Typological Features in Source Language Selection for Cross-Lingual NER on 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: 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?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.