1. A method comprising: training a plurality of hierarchical, parsed-based dialog models comprising a shift-reduce model, a start-complete model, and a connection path model, wherein the plurality of hierarchical, parsed-based dialog models operate incrementally from left to right and only analyze an immediately preceding dialog context; parsing, via a processor, spoken dialogs with a hierarchical, parse-based dialog model from the plurality of hierarchical, parsed-based dialog models, to yield parsed spoken dialogs, wherein the spoken dialogs are annotated to indicate dialog acts, feature vectors, and task/subtask information; constructing a functional task structure of the parsed spoken dialogs, wherein the functional task structure does not comprise a rhetorical structure of the parsed spoken dialogs; predicting a likely next dialog act using the functional task structure, the feature vectors, and the hierarchical, parsed-based dialog model; and selecting a language model for a next utterance based on the likely next dialog act.
2. The method of claim 1 , wherein the shift-reduce model has a stack and a tree which (a) shifts each utterance onto the stack, (b) inspects the stack, and (c) based on the stack inspection, performs a reduce action that creates subtrees in the tree.