Method and system for predicting understanding errors in automated dialog systems
View Patent ↗This invention concerns a method and system for monitoring an automated dialog system for the automatic recognition of language understanding errors based on a user's input communications. The method includes determining whether a probability of understanding the user's input communication exceeds a first thresholds, where if the first threshold is exceeded, further dialog is conducted with the user. Otherwise, the user may be directed to a human for assistance. In another possible embodiment, the method operates as above except that if the probability also exceeds a second threshold, the second threshold being higher than the first, then further dialog is conducted with the user using the current dialog strategy. However, if the probability falls between a first threshold and a second threshold, the dialog strategy may be adapted in order to improve the chances of conducting a successful dialog with the user.
1. A method of monitoring language understanding of a user's input communication in an automated dialog system, comprising:
determining whether a probability of understanding the user's input communication exceeds a first threshold, wherein if the first threshold is exceeded, further dialog is conducted with the user and if the first threshold is not exceeded, the user is routed to a human for assistance; and
determining whether a probability of understanding the user's input communication exceeds a second threshold, the second threshold being greater than the first threshold, wherein if the second threshold is exceeded, further dialog is conducted with the user using a current dialog strategy and if the second threshold is not exceeds, further dialog is conducted with the user using an adapted dialog strategy.
2. The method of claim 1 , wherein the adapted dialog strategy includes one of prompting the user choices and prompting the user to confirm the recognition and understanding data.
3. The method of claim 1 , wherein the user's input communication includes at least one of verbal and nonverbal communications.
4. The method of claim 3 , wherein the nonverbal communications includes at least one of gestures, body movements, head movements, non-responses, text, keyboard entries, graphical user interface entries, and touchscreen entries.
5. The method of claim 1 , wherein the method is used for customer care purposes.
6. The method of claim 1 , wherein the probability is determined using recognition and understanding data derived from the user's input communication.
7. The method of claim 1 , wherein the probability is determined using training data stored in a training database, the training data including at least one of classification models and extracted features.
8. The method of claim 7 , wherein the extracted features are derived from recognition, understanding and dialog data.
9. The method of claim 1 , further comprising:
storing a first dialog exchange in a dialog history database, wherein the first dialog exchange includes a first automated dialog output and the user's first input communication and the further dialog conducted with the user results in a second dialog exchange, wherein the second dialog exchange includes a second dialog output and the user's second input communication; and
determining whether the probability of understanding exceeds the first threshold using the first dialog exchange and the second dialog exchange.
10. The method of claim 9 , wherein the method is recursive in that the determining step determines whether the probability of understanding the user's input exceeds the first threshold using each of the dialog exchanges conducted.
11. The method of claim 1 , further comprising:
receiving the user's input communication;
recognizing portions of the user's input communications; and
providing an input to a language understanding monitor based on applying a confidence function to the recognized portions of the user's input communication.
12. A language understanding monitoring system that operates in an automated dialog system, comprising:
a dialog manager that outputs dialog to the user,
a language understanding monitor that determines whether a probability of understanding the user's input communication exceeds a first threshold, wherein if the first threshold is exceeded, the language understanding monitor prompts the dialog manager to conduct further dialog with the user, and if the first threshold is not exceeded, the language understanding monitor prompts the dialog manager to route the user to a human for assistance and further determines whether a probability of understanding the user's input communication exceeds a second threshold, the second threshold being greater than the first threshold, and if the second threshold is exceeded, the language understanding monitor prompts the dialog manager to conduct further dialog with the user using a current dialog strategy, and if the second threshold is riot exceeded, the language understanding monitor prompts the dialog manager to conduct further dialog with the user using an adaptive dialog strategy.
13. The system of claim 12 , wherein the adapted dialog strategy includes one of prompting the user with choices and prompting the user to confirm the recognition and understanding data.
14. The system of claim 12 , wherein the user's input communication includes at least one of verbal and nonverbal communications.
15. The system of claim 12 , wherein the system is used for customer care purposes.
16. The system of claim 12 , wherein the language understanding monitor determines the probability using recognition data provided by a recognizer and understanding data provided by a language understanding unit, and the recognition and understanding data is derived from the user's input communication.
17. The system of claim 12 , further comprising a training database for storing training data for language understanding, wherein the language understanding monitor determines the probability using the training data stored in the training database, the training data including at least one of classification models and extracted features.
18. The system of claim 17 , wherein the extracted features are derived from recognition, understanding and dialog data.
19. The system of claim 12 , further comprising:
a dialog history database that stores a first dialog exchange, wherein the first dialog exchange includes a first automated dialog output and the user's first input communication, and the further dialog conducted with the user results in a second dialog exchange, the second dialog exchange including a second dialog output and the user's second input communication, and the language understanding monitor determining whether the probability of understanding exceeds the first threshold using the first dialog exchange and the second dialog exchange.
20. The system of claim 19 , wherein the language understanding monitor determines whether the probability of understanding exceeds the first threshold using each of the dialog exchanges conducted.
21. The system of claim 12 , further comprising:
a recognizer that recognizes the user's input communication; and
a language understanding unit that applies a confidence function to the recognized portions of the user's input communication and provides an input to the language understanding monitor.