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

• US 8,612,229 B2
• Filed: 12/15/2005
• Issued: 12/17/2013
• Est. Priority Date: 12/15/2005
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Balchandran, Rajesh, Boyer, Linda M., Lewis, James R., Metz, Brent D.
• Primary Examiner(s): Godbold, Douglas
• Assistant Examiner(s): ORTIZ SANCHEZ, MICHAEL
• Application Number:
  US11/300,799
• Publication Number:
  US 20070143099A1
• Time in Patent Office: 2,924 Days
• Field of Search: 704/243, 704/244, 704/257
• US Class Current: 704/270
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/18 using natural language modelling
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 2015/0638 Interactive procedures
• CPC Class Codes: H04M 2203/2061 Language aspects
• CPC Class Codes: H04M 3/4936 Speech interaction details speech recognition per se G10L15/00

Abstract

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 call to the routing destination. The method can include presenting the example sentence in a help message (126) within an NLU dialogue 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 dialogue 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.

Claims

1. A method for facilitating development of a natural language understanding (NLU) model associated with an NLU application executing on a computer system comprising a combination of hardware and software, the method comprising acts of:

receiving, from a developer of the NLU application, at least one expected user entry and a corresponding desired routing destination;

determining whether the NLU model associates the at least one expected user entry with the desired routing destination, the determining comprising;

interpreting the at least one expected user entry via the NLU model to determine an actual routing destination for the at least one expected user entry, and

comparing the actual routing destination to the desired routing destination;

if it is determined that the actual routing destination of the at least one expected user entry matches 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 a legitimate utterance the user could speak to be routed to the desired routing destination; and

if it is determined that the actual routing destination does not match the desired routing destination;

(i) adding the at least one expected user entry to an NLU entry data set associated with the NLU model, and

(ii) training the NLU model to associate the at least one expected user entry with the desired routing destination.

2. The method of claim 1, wherein the NLU entry data set is an NLU database.

3. The method of claim 1, further comprising:

presenting said at least one expected user entry in a help message of said NLU application as example input to the NLU application.

4. The method of claim 1, further comprising:

if it is determined that the actual routing destination does not match the desired routing destination, presenting a failure dialog to the developer of the NLU application indicating that the actual routing destination does not match to the desired routing destination.

5. The method of claim 1, further comprising:

if it is determined that the actual routing destination matches the desired routing destination, using the at least one expected user entry to strengthen an association between the at least one expected user entry and the desired routing destination in the NLU model.

6. The method of claim 1, wherein training the NLU model to associate the at least one expected user entry with the desired routing destination causes the NLU model to form an association between the at least one expected user entry and the desired routing destination such that a user providing the at least one expected user entry during an interaction with the NLU application is connected to the desired routing destination.

7. The method of claim 3, wherein said help prompt comprises a preamble followed by the at least one expected user entry.

8. The method of claim 1, wherein adding the at least one expected user entry to an NLU entry data set associated with the NLU model comprises:

marking the at least one expected user entry within the NLU entry data set to set a training frequency and/or a statistical weighting associated with the at least one expected user entry.

9. The method of claim 8, wherein training the NLU model to associate the at least one expected user entry with the desired routing destination comprises:

training the NLU model using the NLU entry data set that includes the at least one marked entry;

testing the NLU model with the at least one marked entry; and

biasing the NLU model to reinforce an association between the at least one marked entry and the corresponding desired routing destination.

10. The method of claim 9, wherein said biasing is controlled to prevent the NLU model from incorrectly interpreting entries that were previously correctly interpreted before the biasing.

11. The method of claim 10, wherein said biasing comprises increasing the training frequency associated with the at least one marked entry.

12. The method of claim 9, wherein said biasing stops at a threshold number of maximum iterations.

13. The method of claim 1, wherein:

the at least one expected user entry is one of text or an utterance, and

the at least one expected user entry is one of a phrase or a sentence.

14. The method of claim 8, further comprising increasing the statistical weighting associated with the at least one marked entry relative to another entry in the NLU entry data set.

15. At least one non-transitory computer-readable medium encoded with instructions that, when executed by a computer system, cause the computer system to perform a method for facilitating development of a natural language understanding (NLU) model associated with an NLU application executing on a computer system comprising a combination of hardware and, the method comprising acts of:

receiving, from a developer of the NLU application, at least one expected user entry expected user entry and a corresponding to-a desired routing destination;

determining whether the NLU model associates the at least one expected user entry with the desired routing destination, the determining comprising;

interpreting the at least one expected user entry via the NLU model to determine an actual routing destination for the at least one expected user entry, and expected user entry, and

comparing the actual routing destination to the desired routing destination;

if it is determined that the actual routing destination does not match the desired routing destination of the at least one expected user entry matches 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 a legitimate utterance the user could speak to be routed to the desired routing destination; and

if it is determined that the actual routing destination does not match the desired routing destination;

(i) adding the at least one expected user entry to an NLU entry data set associated with the NLU model, and

(ii) training the NLU model to associate the at least one expected user entry with the desired routing destination.

16. The at least one computer-readable medium of claim 15, further comprising:

if it is determined that the actual routing destination matches the desired routing destination, using the at least one expected user entry to strengthen an association between the at least one expected user entry and the desired routing destination in the NLU model.

17. The at least one computer-readable medium of claim 15, wherein training the NLU model to associate the at least one expected user entry with the desired routing destination causes the NLU model to form an association between the at least one expected user entry and the desired routing destination such that a user providing the at least one expected user entry during an interaction with the NLU application is connected to the desired routing destination.

18. The at least one computer-readable medium of claim 15, wherein the method further comprises increasing a statistical weighting of the at least one expected user entry relative to another entry in the NLU data entry set.

19. An apparatus for facilitating development of a computer-implemented natural language understanding (NLU) model associated with an NLU application, the apparatus comprising:

at least one computer-readable medium encoded with instructions; and

at least one processing unit coupled to the at least one computer-readable medium, wherein upon execution of the instructions by the at least one processing unit, the at least one processing unit;

receives, from a developer of the NLU application, at least one expected user entry and a corresponding desired routing destination;

determines whether the NLU model associates the at least one expected user entry with the desired routing destination, the determining comprising;

interpreting the at least one expected user entry via the NLU model to determine an actual routing destination for the at least one expected user entry, and

comparing the actual routing destination to the desired routing destination;

if it is determined that the actual routing destination of the at least one expected user entry matches the desired routing destination, selects the at least one expected user entry for presentation to a user during a help prompt of the NLU application as an example of a legitimate utterance the user could speak to be routed to the desired routing destination; and

if it is determined that the actual routing destination does not match the desired routing destination;

adds the at least one expected user entry to an NLU entry data set associated with the NLU model, and

trains the NLU model to associate the at least one expected user entry with the desired routing destination.

20. The apparatus of claim 19, wherein if it is determined that the actual routing destination matches the desired routing destination, the at least one processing unit:

uses the at least one expected user entry to strengthen an association between the at least one expected user entry and the desired routing destination in the NLU model.

21. The apparatus of claim 19, wherein training the NLU model to associate the at least one expected user entry with the desired routing destination causes the NLU model to form an association between the at least one expected user entry and the desired routing destination such that a user providing the at least one expected user entry during an interaction with the NLU application is connected to the desired routing destination.

22. The apparatus of claim 19, wherein the at least one processing unit:

increases a statistical weighting of the at least one expected user entry relative to another entry in the NLU data entry set.