Dialogue system, a dialogue method and a method of adapting a dialogue system

• US 10,446,148 B2
• Filed: 08/28/2017
• Issued: 10/15/2019
• Est. Priority Date: 02/13/2017
• Status: Active Grant

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Papangelis, Alexandros, Stylianou, Ioannis
• Primary Examiner(s): Thomas-Homescu, Anne L
• Application Number:
  US15/687,793
• Publication Number:
  US 20180233143A1
• Time in Patent Office: 778 Days
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 40/237 Lexical tools
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/16 using artificial neural networks
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue

Abstract

A dialog system comprising:

• • an input for receiving data relating to a speech or text signal originating from a user;
  • an output for outputting information specified by an action; and
  • a processor configured to:
    • update one or more system states based on the input data using one or more state tracker models, wherein the one or more system states comprise probability values associated with each of a plurality of possible values for each of a plurality of categories, wherein a category corresponds to a subject that the speech or text signal may relate to and can take on one or more values from a set of values;
    • determine an action function and determine an action function input by inputting information generated using the system state and a set of stored information into a policy model, the set of stored information comprising a plurality of action functions;
    • output information specified by the determined action function and the determined action function input at the output.

Claims

1. A method of adapting a dialogue system, by using the system repeatedly to run through dialogues with a human or a simulated human and providing a performance indicator for each dialogue, and wherein the method further comprises:

receiving data relating to a speech or text signal in a dialogue;

updating a system state based on the input data using a state tracker model, wherein the system state comprises probability values associated with each of a plurality of possible values for each of a plurality of categories, wherein a category corresponds to a subject that the speech or text signal may relate to and can take on one or more values from a set of values;

determining an action function and determining an action function input by inputting information generated using the system state and a set of stored information into a policy model, the set of stored information comprising a plurality of action functions;

outputting information specified by the determined action function and the determined action function input at the output;

adapting the policy model to increase the performance indicator; and

for each category present in the dialogues, determining a cumulative moving average of the ratio of the performance indicator achieved to the maximum possible performance indicator as an estimate of category importance, wherein the importance is used as one of a second set of parameters.

2. The method according to claim 1, wherein the inputted information generated using the system state or the set of stored information comprises a set of possible action function inputs, and wherein determining an action function and determining an action function input comprises:

for each possible action function input generating a vector of values, wherein each value in each vector corresponds to an action function and is an estimate of the dialogue performance if the action function and action function input are used to generate the output information, the values being generated from stored parameters;

wherein the determined action function and action function input corresponds to the action function corresponding to the highest value from all of the vectors; and

wherein adapting the policy model comprises updating the stored parameters based on the performance indicator.

3. The method according to claim 1, wherein the inputted information generated using the system state or the set of stored information comprises a set of possible action function inputs, wherein the action function inputs comprise categories, and further comprising:

identifying one or more relevant categories;

wherein the system state is a reduced system state, comprising the probability values associated with one or more of the plurality of possible values for each of the relevant categories.

4. The method according to claim 3, wherein the one or more relevant categories are identified based on at least part of an input system state information using an identifier model.

5. A method of adapting a dialogue system, by using the system repeatedly to run through dialogues with a human or a simulated human and providing a performance indicator for each dialogue, and wherein the method further comprises:

receiving data relating to a speech or text signal in a dialogue;

updating a system state based on the input data using a state tracker model, wherein the system state comprises probability values associated with each of a plurality of possible values for each of a plurality of categories, wherein a category corresponds to a subject that the speech or text signal may relate to and can take on one or more values from a set of values;

determining an action function and determining an action function input by inputting information generated using the system state and a set of stored information into a policy model, the set of stored information comprising a plurality of action functions;

outputting information specified by the determined action function and the determined action function input at the output;

adapting the policy model to increase the performance indicator; and

for each category in the dialogues, determining a cumulative moving average of the relative position in the dialogue as an estimate of category priority, wherein the priority is used as one of a second set of parameters.