21. A method of generating annotated training data to train a natural language understanding (NLU) system having one or more models, comprising: generating a proposed annotation with the NLU system for each of one or more units of unannotated training data; displaying the proposed annotations for user verification or correction to obtain a user-confirmed annotation; training the NLU system with the user-confirmed annotation; identifying inconsistencies between the user-confirmed annotation and prior annotations; displaying a user actuable delete node input which, when actuated, deletes a child node; displaying 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 a 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 one or more models of the natural language understanding system.
21. A method of generating annotated training data to train a natural language understanding (NLU) system having one or more models, comprising: generating a proposed annotation with the NLU system for each of one or more units of unannotated training data; displaying the proposed annotations for user verification or correction to obtain a user-confirmed annotation; training the NLU system with the user-confirmed annotation; identifying inconsistencies between the user-confirmed annotation and prior annotations; displaying a user actuable delete node input which, when actuated, deletes a child node; displaying 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 a 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 one or more models of the natural language understanding system. 22. The method of claim 21 and further comprising: if an inconsistency is identified, displaying the user-confirmed annotation visually contrasting inconsistent portions of the user-confirmed annotation.