  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. At its core, UXLA performs simultaneous self-training with data
augmentation and unsupervised sample selection. To show its effectiveness, we
conduct extensive experiments on three diverse zero-resource cross-lingual
transfer tasks. UXLA achieves SoTA results in all the tasks, outperforming the
baselines by a good margin. With an in-depth framework dissection, we
demonstrate the cumulative contributions of different components to its
success.
