Cross-lingual transfer learning, which uses machine learning models trained in one language to bootstrap models in another, benefits from algorithms that select high-value training data in the source language.

Transfer of a model co-trained on intent classification and slot (variable) tagging halved the data required to achieve a given level of performance.