Multi-source Teacher-Student Learning for Cross-lingual NER in Low-Resource Languages with Label Noise

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 multi-source teacher-student learning improve cross-lingual NER accuracy on low-resource languages compared to single-source transfer under high label noise conditions?

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