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.
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. 9. The method of claim 1 , further comprising training a slot model with the training data comprising three or more of: (a) a first portion of the sentences that were translated using machine translation on abstract sentences, followed by slot filling; (b) a second portion of the sentences whose carrier phrases were translated using a human, followed by slot values replaced by slot filling; (c) a third portion of the sentences that were translated using a straight translation; or (d) a fourth portion of the sentences wherein each slot value of the sentences in the third portion is replaced by an entity from slot filling.