Abstract: Systems and techniques for generating natural language understanding (NLU) models are described. A developer of an NLU model may provide data representing runtime NLU functionality. For example, a developer may provide one or more sample natural language user inputs. The NLU model generation system may expand data, provided by the developer, to result in a more robust NLU model for use at runtime. For example, the NLU model generation system may expand sample natural language user inputs, may translate sample natural language user inputs into other languages, etc. The present disclosure also provides a mechanism for transitioning between using NLU models of a first NLU model generation system and NLU models of a second NLU model generation system.

Abstract: Example embodiments provide techniques for configuring a natural language processing (NLU) system for new functionalities using sample user inputs provided by a developer. The system may determine intent labels associated with the sample user inputs using previous user inputs previously processed by the NLU system. The system may configure a new NLU model, using the NLU model associated with the intent label determined by the system, where the new NLU model is configured to enable invocation of the new functionality.