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