Multi-source Cross-lingual NER via Teacher-Student Learning: Low-Resource Evaluation on CoNLL-2003

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 performance of multi-source cross-lingual NER via teacher-student learning compare to single-source baselines when evaluated on low-resource languages using the CoNLL-2003 benchmark?

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