1. A computer-implemented method, performed by at least one processor, for using training data in a first language to create training data in a second language, comprising: accessing the training data in the first language that include sentences that each comprises one or more carrier phrases, and one or more slot labels with slot values; performing slot abstraction on at least a portion of the training data to create a first plurality of abstract sentences that each comprises one or more carrier phrases, and one or more abstract tokens that replace the slot labels and the slot values; translating at least partially through machine translation the carrier phrases to the second language to generate a second plurality of abstract sentences in the second language; accessing a database of a plurality of locale-dependent entities based on a locale corresponding to the second language; replacing each of abstract tokens in the second plurality of abstract sentences in the second language with multiple locale-dependent entities from the plurality of locale-dependent entities for the slot type, in order to create a plurality of filled translated sentences for inclusion in the training data in the second language; training a locale-dependent statistical model based on the training data in the second language; and recognizing speech in the second language based on the locale-dependent statistical model.