UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP

Understand

Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks.

• However, annotated data for every target task in every target language is rare, especially for low-resource languages.
• We propose UXLA, a novel unsupervised data augmentation framework for zero-resource transfer learning scenarios.
• In particular, UXLA aims to solve cross-lingual adaptation problems from a source language task distribution to an unknown target language task distribution, assuming no training label in the target language.