1. A computer-implemented method of using alternate recognition hypotheses to improve whole-dialog understanding accuracy, the method comprising: receiving an utterance as part of a user dialog; generating an N-best list of recognition hypotheses for the user dialog; selecting an underlying user intention based on a belief distribution across the generated N-best list and at least one contextually similar N-best list; and responding to the user based on the selected underlying user intention.