SYSTEM AND METHOD FOR GENERATING MANUALLY DESIGNED AND AUTOMATICALLY OPTIMIZED SPOKEN DIALOG SYSTEMS

• US 20110010164A1
• Filed: 07/13/2009
• Published: 01/13/2011
• Est. Priority Date: 07/13/2009
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Inventors: WILLIAMS, Jason
• Granted Patent: US 8,793,119 B2
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/56 Natural language generation
• CPC Class Codes: G10L 15/063 Training

Abstract

Disclosed herein are systems, computer-implemented methods, and tangible computer-readable storage media for generating a natural language spoken dialog system. The method includes nominating a set of allowed dialog actions and a set of contextual features at each turn in a dialog, and selecting an optimal action from the set of nominated allowed dialog actions using a machine learning algorithm. The method includes generating a response based on the selected optimal action at each turn in the dialog. The set of manually nominated allowed dialog actions can incorporate a set of business rules. Prompt wordings in the generated natural language spoken dialog system can be tailored to a current context while following the set of business rules. A compression label can represent at least one of the manually nominated allowed dialog actions.

Claims

1. A computer-implemented method of generating a natural language spoken dialog system, the method comprising:

at each turn in a dialog, nominating a set of allowed dialog actions and a set of contextual features;

selecting an optimal action from the set of nominated allowed dialog actions using a machine learning algorithm; and

generating a response based on the selected optimal action at each turn in the dialog.

2. The method of claim 1, wherein the machine learning algorithm uses reinforcement learning.

3. The method of claim 1, wherein the machine learning algorithm is partially observable Markov decision process (POMDP) based.

4. The method of claim 1, wherein the set of nominated allowed dialog actions incorporates a set of business rules.

5. The method of claim 4, wherein prompt wordings in the generated natural language spoken dialog system are tailored to a current context while following the set of business rules.

6. The method of claim 1, wherein a compression label represents at least one of the nominated allowed dialog actions.

7. A system for generating a natural language spoken dialog system, the system comprising:

a processor;

a module configured to control the processor to nominate allowed dialog actions and a set of contextual features at each turn of a dialog;

a module configured to control the processor to select an optimal action from the set of nominated allowed dialog actions at each dialog turn based on machine learning algorithm; and

a module configured to control the processor to generate a response based on the selected optimal action at each turn in the dialog.

8. The system of claim 7, wherein the system uses reinforcement learning.

9. The system of claim 7, wherein the system is partially observable Markov decision process (POMDP) based.

10. The system of claim 7, wherein the set of manually nominated allowed dialog actions incorporates a set of business rules.

11. The system of claim 10, wherein prompt wordings in the generated natural language spoken dialog system are tailored to a current context while following the set of business rules.

12. The system of claim 7, wherein a compression label represents at least one of the manually nominated allowed dialog actions.

13. A tangible computer-readable storage medium storing a computer program having instructions for controlling a processor to generate a natural language spoken dialog system, the instructions comprising:

nominating allowed dialog actions and a set of contextual features at each turn of a dialog;

selecting an optimal action from the set of manually nominated allowed dialog actions at each turn of a dialog based on machine learning algorithm; and

generating a spoken dialog system based on a process of selecting optimal actions at each dialog turn.

14. The tangible computer-readable storage medium of claim 13, wherein the machine learning algorithm uses reinforcement learning.

15. The tangible computer-readable storage medium of claim 13, wherein the machine learning algorithm is partially observable Markov decision process (POMDP) based.

16. The tangible computer-readable storage medium of claim 13, wherein the set of manually nominated allowed dialog actions incorporates a set of business rules.

17. The tangible computer-readable storage medium of claim 16, wherein prompt wordings in the generated natural language spoken dialog system are tailored to a current context while following the set of business rules.