Method and system for conveying an example in a natural language understanding application

摘要

A method (300) and system (100) is provided to add the creation of examples at a developer level in the generation of Natural Language Understanding (NLU) models, tying the examples into a NLU sentence database (130), automatically validating (310) a correct outcome of using the examples, and automatically resolving (316) problems the user has using the examples. The method (300) can convey examples of what a caller can say to a Natural Language Understanding (NLU) application. The method includes entering at least one example associated with an existing routing destination, and ensuring an NLU model correctly interprets the example unambiguously for correctly routing a can to the routing destination. The method can include presenting the example sentence in a help message (126) within an NLU dialog as an example of what a caller can say for connecting the caller to a desired routing destination. The method can also include presented a failure dialog for displaying at least one example that failed to be properly interpreted to ensure that ambiguous or incorrect examples are not presented in a help message.

主权项

1. A system for facilitating development, in a natural language understanding (NLU) application development environment, of an NLU model associated with an NLU application, the system comprising:
at least one processor; an NLU database storing information used for training one or more NLU models; at least one non-transitory computer-readable storage medium encoded with instructions that, when executed by the at least one processor, cause the at least one processor to perform a method comprising: obtaining, from a developer of the NLU application, at least one expected user entry and a corresponding desired routing destination; adding the at least one expected user entry and the corresponding desired routing information to the NLU database; applying the NLU model to the at least one expected user entry to determine whether the NLU model associates the at least one expected user entry with the desired routing destination; when it is determined that the NLU model associates the at least one expected user entry with the desired routing destination, selecting the at least one expected user entry for presentation to a user during a help prompt of the NLU application as an example of input the user could provide to be routed to the desired routing destination; when it is determined that the NLU model does not associate the at least one expected user entry with the desired routing destination, accessing training data in the NLU database, the training data including the at least one expected user entry and the corresponding desired routing information, and training the NLU model using the training data accessed in the NLU database to associate the at least one expected user entry with the desired routing destination.