29. A method of generating annotated training data for training a natural language understanding (NLU) system having at least one model, comprising: generating a proposed annotation for a unit of unannotated training data; calculating a confidence measure for a plurality of different portions of the proposed annotation; displaying the proposed annotation by visually contrasting portions that have a corresponding confidence measure that falls below a threshold level; displaying user actuable inputs for user correction or verification of the proposed annotation, user actuable inputs comprising: one or more user actuable node inputs for annotation alternatives; a user actuable delete node input which, when actuated, deletes a child node; and a user actuable add node input which, when actuated, adds a child node; displaying a plurality of alternative proposed annotations to data portions associated with the child node in response to that child node being deleted, such that the user is enabled to select one of the alternative proposed annotations from among the plurality of alternative proposed annotations, and the user-selected alternative proposed annotation is incorporated into the annotated training data; and displaying an indication of a volume of training data used to train a plurality of different portions of the at least one model of the natural language understanding system.
29. A method of generating annotated training data for training a natural language understanding (NLU) system having at least one model, comprising: generating a proposed annotation for a unit of unannotated training data; calculating a confidence measure for a plurality of different portions of the proposed annotation; displaying the proposed annotation by visually contrasting portions that have a corresponding confidence measure that falls below a threshold level; displaying user actuable inputs for user correction or verification of the proposed annotation, user actuable inputs comprising: one or more user actuable node inputs for annotation alternatives; a user actuable delete node input which, when actuated, deletes a child node; and a user actuable add node input which, when actuated, adds a child node; displaying a plurality of alternative proposed annotations to data portions associated with the child node in response to that child node being deleted, such that the user is enabled to select one of the alternative proposed annotations from among the plurality of alternative proposed annotations, and the user-selected alternative proposed annotation is incorporated into the annotated training data; and displaying an indication of a volume of training data used to train a plurality of different portions of the at least one model of the natural language understanding system. 30. The method of claim 29 wherein displaying comprises: visually contrasting only a portion having the lowest confidence measure.