DIALOG SYSTEM WITH SELF-LEARNING NATURAL LANGUAGE UNDERSTANDING
First Claim
1. A method for a dialog system comprising a client device configured with a text input method to receive user input, and a first natural language understanding (NLU) model configured to communicate with the server, the user input involving at least one of text input typed by the user or Automatic Speech Recognition (ASR) output obtained from the user speech, and a server comprising a second NLU model, wherein the first NLU model and the second NLU model are different, the method comprising:
- determining NLU results of an input dialog at the client device from applying the first NLU model;
for a confidence score of the NLU results obtained from applying the first NLU model not meeting a threshold, having the client device access the server to execute an NLU process by using the second NLU model on the input dialog; and
for the confidence score of the NLU results obtained from applying the first NLU model meeting the threshold, having the client device execute actions based on the NLU results obtained from applying the first NLU model.
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Abstract
Example implementations described herein are directed to a dialog system with self-learning natural language understanding (NLU), involving a client-server configuration. If the NLU results in the client is not confident, the NLU will be done again in the server. In the dialog system, the human user and the system communicate via speech or text information. The examples of such products include robots, interactive voice response system (IVR) for call centers, voice-enabled personal devices, car navigation system, smart phones, and voice input devices in the work environments where the human operator cannot operate the devices by hands.
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Citations
20 Claims
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1. A method for a dialog system comprising a client device configured with a text input method to receive user input, and a first natural language understanding (NLU) model configured to communicate with the server, the user input involving at least one of text input typed by the user or Automatic Speech Recognition (ASR) output obtained from the user speech, and a server comprising a second NLU model, wherein the first NLU model and the second NLU model are different, the method comprising:
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determining NLU results of an input dialog at the client device from applying the first NLU model; for a confidence score of the NLU results obtained from applying the first NLU model not meeting a threshold, having the client device access the server to execute an NLU process by using the second NLU model on the input dialog; and for the confidence score of the NLU results obtained from applying the first NLU model meeting the threshold, having the client device execute actions based on the NLU results obtained from applying the first NLU model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification