DeMuX: Data-efficient Multilingual Learning

Understand

We consider the task of optimally fine-tuning pre-trained multilingual models, given small amounts of unlabelled target data and an annotation budget.

• In this paper, we introduce DEMUX, a framework that prescribes the exact data-points to label from vast amounts of unlabelled multilingual data, having unknown degrees of overlap with the target set.
• Unlike most prior works, our end-to-end framework is language-agnostic, accounts for model representations, and supports multilingual target configurations.
• Our active learning strategies rely upon distance and uncertainty measures to select task-specific neighbors that are most informative to label, given a model.