Determining domains for natural language understanding
First Claim
1. A computer-implemented method comprising:
- receiving text corresponding to a query;
processing the text with a first natural-language understanding (NLU) component to determine a first NLU result;
at least partially in parallel with processing the text with the first NLU component, processing the text with a first trained model to determine;
a first intent category score corresponding to a first likelihood that the text is associated with a first intent category, anda second intent category score corresponding to a second likelihood that the text is associated with a second intent category;
processing the first intent category score and the second intent category score with a second trained model to select;
a first NLU domain associated with the first intent category, anda second NLU domain associated with the second intent category; and
after processing the first intent category score and the second intent category score with the second trained model;
processing the text with a second NLU component associated with the first NLU domain to determine a second NLU result,processing the text with a third NLU component associated with the second NLU domain to determine a third NLU result,processing the first NLU result, the second NLU result, and the third NLU result to select the second NLU result, andcausing a first command associated with the second NLU result to be executed.
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Abstract
A system capable of performing natural language understanding (NLU) using different application domains in parallel. A model takes incoming query text and determines a list of potential supplemental intent categories corresponding to the text. Supplemental applications within those categories are then identified as likely candidates for responding to the query. Application specific domains, including NLU components for the particular supplemental applications, are then activated and process the query text in parallel. Further, certain system default domains may also process incoming queries substantially in parallel with the supplemental applications. The different results are scored and ranked to determine highest scoring NLU results.
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Citations
21 Claims
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1. A computer-implemented method comprising:
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receiving text corresponding to a query; processing the text with a first natural-language understanding (NLU) component to determine a first NLU result; at least partially in parallel with processing the text with the first NLU component, processing the text with a first trained model to determine; a first intent category score corresponding to a first likelihood that the text is associated with a first intent category, and a second intent category score corresponding to a second likelihood that the text is associated with a second intent category; processing the first intent category score and the second intent category score with a second trained model to select; a first NLU domain associated with the first intent category, and a second NLU domain associated with the second intent category; and after processing the first intent category score and the second intent category score with the second trained model; processing the text with a second NLU component associated with the first NLU domain to determine a second NLU result, processing the text with a third NLU component associated with the second NLU domain to determine a third NLU result, processing the first NLU result, the second NLU result, and the third NLU result to select the second NLU result, and causing a first command associated with the second NLU result to be executed. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system comprising:
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at least one processor; and a memory including instructions that, when executed by the at least one processor, cause the system to; receive text corresponding to a query; process the text with a first natural-language understanding (NLU) component to determine a first NLU result; at least partially in parallel with processing the text with the first NLU component, process the text with a first trained model to determine; a first intent category score corresponding to a first likelihood that the text is associated with a first intent category, and a second intent category score corresponding to a second likelihood that the text is associated with a second intent category; process the first intent category score and the second intent category score with a second trained model to select; a first NLU domain associated with the first intent category, and a second NLU domain associated with the second intent category; and after processing the first intent category score and the second intent category score with the second trained model; process the text with a second NLU component associated with the first NLU domain to determine a second NLU result, process the text with a third NLU component associated with the second NLU domain to determine a third NLU result, process the first NLU result, the second NLU result, and the third NLU result to select the second NLU result, and cause a first command associated with the second NLU result to be executed. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
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Specification