Zero-Shot Cross-Lingual Transfer with Meta Learning

Understand

Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve downstream task performance.

• This is particularly important for multilingual applications, as most languages in the world are under-resourced.
• Here, we consider the setting of training models on multiple different languages at the same time, when little or no data is available for languages other than English.
• We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first.