Natural language generation based on user speech style
First Claim
1. A method of generating a natural language generation (NLG) output, wherein the method comprises:
- receiving speech signals from a user at a microphone of a client device;
determining a requested communication goal and at least one inputted communication value based on the received speech signals;
determining to use a static natural language generation (NLG) template or a dynamic NLG template to generate an NLG output, wherein the determination of whether to use a static NLG template or a dynamic NLG template is made using a neural network NLG template selection process;
selecting an NLG template after the determination of whether to use a static NLG template or a dynamic NLG template; and
generating an NLG output based on the selected NLG template.
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Abstract
A system and method of generating a natural language generation (NLG) output, wherein the method includes: receiving speech signals from a user at a microphone of a client device; determining a requested communication goal and at least one inputted communication value based on the received speech signals; determining to use a static natural language generation (NLG) template or a dynamic NLG template to generate an NLG output, wherein the determination of whether to use a static NLG template or a dynamic NLG template is made using a neural network NLG template selection process; selecting an NLG template after the determination of whether to use a static NLG template or a dynamic NLG template; and generating an NLG output based on the selected NLG template.
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Citations
20 Claims
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1. A method of generating a natural language generation (NLG) output, wherein the method comprises:
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receiving speech signals from a user at a microphone of a client device; determining a requested communication goal and at least one inputted communication value based on the received speech signals; determining to use a static natural language generation (NLG) template or a dynamic NLG template to generate an NLG output, wherein the determination of whether to use a static NLG template or a dynamic NLG template is made using a neural network NLG template selection process; selecting an NLG template after the determination of whether to use a static NLG template or a dynamic NLG template; and generating an NLG output based on the selected NLG template. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A method of generating a natural language generation (NLG) output, wherein the method comprises:
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receiving speech signals from a user at a microphone of a client device; identifying a user or a likely user that transmitted the speech signals; determining a communication goal and at least one inputted communication value based on the received speech signals; determining whether to use a static NLG template or dynamic NLG template for use in generating an NLG output, wherein the determination of whether to use a static NLG template or dynamic NLG template is made using a neural network NLG template selection process, wherein the neural network NLG template selection process uses an artificial neural network to resolve a set of inputs to select whether to use a static NLG template or dynamic NLG template for use in generating an NLG output, and wherein the set of inputs include the communication goal, the inputted communication values, and either a user history that is associated with the user or a user profile that is associated with the user; where it is determined to use a static NLG template, then selecting a static NLG template; when it is determined to use a dynamic NLG template, then generating the dynamic NLG template; and generating the NLG output using the selected NLG template. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification