Method and system for conveying an example in a natural language understanding application
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.
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.
2. The system of claim 1 , wherein the applying comprises:
interpreting, via the NLU model, the at least one expected user entry to determine an actual routing destination for the at least one expected user entry; and
determining whether the NLU model associates the at least one expected user entry with the desired routing destination by comparing the actual routing destination to the desired routing destination.
3. The system of claim 2 , wherein, when it is determined that the NLU model does not associate the at least one expected user entry with the desired routing destination, the method further comprises:
presenting a failure dialog to the developer of the NLU application indicating that the actual routing destination does not match the desired routing destination.
4. The system of claim 1 , wherein training the NLU model to associate the at least one expected user entry with the desired routing destination comprises:
training the NLU model in accordance with a specified training frequency and/or a statistical weighting for the at least one expected user entry.
5. The system of claim 1 , wherein when it is determined that the NLU model does not associate the at least one expected user entry with the desired routing destination, the method further comprises:
adding the at least one expected user entry to an NLU entry data set associated with the NLU model; and
training the NLU model using the data in the NLU entry data set.
6. The system of claim 1 , wherein the method further comprises:
presenting said at least one expected user entry in a help message of said NLU application as example input to the NLU application.
7. The system of claim 6 , wherein the help message comprises a preamble followed by the at least one expected user entry.