Contextual language understanding for multi-turn language tasks
First Claim
1. A system, comprising:
- at least one processor; and
a memory encoding computer executable instructions that, when executed by the at least one processor, perform a method, comprising;
receiving a natural language expression;
determining, using a single-turn model, a first weighted prediction of at least one of a domain classification, intent classification, and slot type of the natural language expression;
determining, using a multi-turn model, a second weighted prediction of at least one of a domain classification, intent classification, and slot type of the natural language expression;
generating a final prediction based, at least in part, on the first weighted prediction and the second weighted prediction; and
performing an action based on the final prediction.
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Abstract
Methods and systems are provided for contextual language understanding. A natural language expression may be received at a single-turn model and a multi-turn model for determining an intent of a user. For example, the single-turn model may determine a first prediction of at least one of a domain classification, intent classification, and slot type of the natural language expression. The multi-turn model may determine a second prediction of at least one of a domain classification, intent classification, and slot type of the natural language expression. The first prediction and the second prediction may be combined to produce a final prediction relative to the intent of the natural language expression. An action may be performed based on the final prediction of the natural language expression.
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Citations
20 Claims
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1. A system, comprising:
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at least one processor; and a memory encoding computer executable instructions that, when executed by the at least one processor, perform a method, comprising; receiving a natural language expression; determining, using a single-turn model, a first weighted prediction of at least one of a domain classification, intent classification, and slot type of the natural language expression; determining, using a multi-turn model, a second weighted prediction of at least one of a domain classification, intent classification, and slot type of the natural language expression; generating a final prediction based, at least in part, on the first weighted prediction and the second weighted prediction; and performing an action based on the final prediction. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method, comprising:
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receiving a first natural language expression; receiving a second natural language expression; generating a first prediction by predicting at least one of a first domain classification, a first intent classification, and a first slot type of each of the first natural language expression and the second natural language expression using a single-turn model; generating a second prediction by predicting at least one of a second domain classification, a second intent classification, and a second slot type of each of the first natural language expression and the second natural language expression using a multi-turn model; combining the first prediction and the second prediction to produce a final prediction; and performing an action based, at least in part, on the final prediction. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A method, comprising:
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receiving a language input; providing the language input to a single-turn model to determine a first weighted prediction of one or more of a domain classification and an intent classification associated with the language input; providing the language input to a multi-turn model to determine a second weighted prediction of one or more of the domain classification and the intent classification; generating a final prediction based on the first weighted prediction and the second weighted prediction; and performing an action based on the final prediction. - View Dependent Claims (20)
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Specification