Using generic predictive models for slot values in language modeling

• US 20070219974A1
• Filed: 03/17/2006
• Published: 09/20/2007
• Est. Priority Date: 03/17/2006
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Paek, Timothy, Chickering, David
• Granted Patent: US 8,032,375 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

A generic predictive argument model that can be applied to a set of slot values to predict a target slot value is provided. The generic predictive argument model can predict whether or not a particular value or item is the intended target of the user command given various features. A prediction for each of the slot values can then be normalized to infer a distribution over all values or items. For any set of slot values (e.g., contacts), a number of binary variables are created that indicate whether or not each specific slot value was the intended target. For each slot value, a set of input features can be employed to predict the corresponding binary variable. These input features are generic properties of the contact that are “instantiated” based on properties of the contact (e.g., contact-specific features). These contact-specific features can be stored in a user data store.

Claims

1. A system for the prediction of a target slot value of a user goal, comprising:

a user data store that stores information regarding user behavior; and

,

an argument model that employs statistical modeling and the information stored in the user data store, the argument model is applied to each of a set of slot values to predict the target slot value.

2. The system of claim 1, statistical modeling of the predictive user model is performed without regard to the substantive content of the user input.

3. The system of claim 1, for each of the set of slot values, the argument model determines a probability that the particular slot value is the target slot value.

4. The system of claim 3, the argument model further infers a probability distribution over the set of slot values.

5. The system of claim 1, the argument model for unseen values is uniform.

6. The system of claim 1, the information stored in the user data store comprises a plurality of features associated with the particular user'"'"'s behavior.

7. The system of claim 6, the features comprising at least one of periodic, contact-specific, predicate-related, time-related and device-related features.

8. The system of claim 1, the argument model comprises a conditional model.

9. The system of claim 8, the conditional model is a decision tree.

10. The system of claim 1, the argument model comprises a generative model.

11. The system of claim 10, the generative model is a naï

ve-Bayes model.

12. The system of claim 1, the argument model is learned for a general user population.

13. The system of claim 1, at least one of a parameter and a structure of the argument model is updated in an online manner.

14. The system of claim 13, the online update is performed according to a learning rate.

15. The system of claim 1 employed for command/control of a personal device, the personal device comprising a desktop computer system, a notebook computer system, a personal digital assistant, a media player, a mobile phone, a smart phone and a remote control.

16. A method for prediction of a target slot value of a user goal for command/control of a personal device, comprising:

for each of a set of slot values, applying a generic argument model to obtain a probability that the slot value is the target slot value; and

,

normalizing the probabilities to infer a distribution over the set of slot values; and

,

employing the inferred probability distribution to identify the target slot value.

17. The method of claim 16, the probability distribution determined by the argument model is independent of a speech input.

18. The method of claim 16, the set of slot values comprises seen slot values.

19. A language model system for prediction of a user goal for command/control of a personal device, comprising:

a predictive user model that comprises an argument model that employs statistical modeling and information stored in a user data store, the argument model is applied to each of a set of slot values to predict a target slot value, the predictive user model providing a probability distribution;

a speech recognition component that provides a probability distribution associated with a speech input; and

,

a language model that determines the user goal based, at least in part, upon the probability distributions provided by the predictive user model and the speech recognition component.

20. The system of claim 19, the information stored in the user data store comprises a plurality of features associated with the particular user'"'"'s behavior, the features comprising at least one of periodic, contact-specific, predicate-related, time-related and device-related features.