19. A computer-implemented method of training a natural language understanding (NLU) model, the method comprising: generating a plurality of segmentations of training data, for a plurality of slots and a plurality of preterminals derived from a schema; training a statistical model using a processor of the computer for mapping a portion of a natural language input to a preterminal corresponding to at least one of the segmentations; and training a statistical task model using the processor modeling a prior probabability of tasks derived from the schema; wherein generating a plurality of segmentations comprises associating slots and preterminals with portions of the training data and wherein training a statistical model comprises generating a statistical model for each of the plurality of preterminals, wherein generating a statistical model for each of the plurality of preterminals comprises: selecting a preterminal; and generating a statistical model for the selected preterminal using the portion of the training data associated with the selected preterminal as training data for the statistical model.
19. A computer-implemented method of training a natural language understanding (NLU) model, the method comprising: generating a plurality of segmentations of training data, for a plurality of slots and a plurality of preterminals derived from a schema; training a statistical model using a processor of the computer for mapping a portion of a natural language input to a preterminal corresponding to at least one of the segmentations; and training a statistical task model using the processor modeling a prior probabability of tasks derived from the schema; wherein generating a plurality of segmentations comprises associating slots and preterminals with portions of the training data and wherein training a statistical model comprises generating a statistical model for each of the plurality of preterminals, wherein generating a statistical model for each of the plurality of preterminals comprises: selecting a preterminal; and generating a statistical model for the selected preterminal using the portion of the training data associated with the selected preterminal as training data for the statistical model. 23. The method of claim 19 wherein the training data comprises semantically annotated training data and further comprising: accessing a probabilistic library grammar; and adapting probabilities in the probabilistic library grammar based on the semantically annotated training data.