Apple Inc.

Deepak Muralidharan

Xiao Charles Yang

Lin Li

Mubarak Ali Seyed Ibrahim

Justine Kao

Ashish Garg

Gagan Aneja

Yingjie Zheng

Lavanya Colinjivadi Viswanathan

John G Keesling

Qiwei Sun

Xinjie Di

AtishKumar Kothari

William Pui Lum Li

In an exemplary process for interpreting spoken requests, audio input containing a user utterance is received. In accordance with a determination that a text representation of the user utterance does not exactly match any of a plurality of user-defined invocation phrases, the process determines whether a comparison between the text representation and a user-defined invocation phrase of the plurality of user-defined invocation phrases satisfies one or more rule-based conditions. In accordance with a determination that the comparison between the text representation and the user-defined invocation phrase satisfies the one or more rule-based conditions, the text representation and the user-defined invocation phrase is processed using a machine-learned model to determine a score representing a degree of semantic equivalence between the text representation and the user-defined invocation phrase. In accordance with a determination that the score satisfies a threshold condition, a predefined task corresponding to the user-defined invocation phrase is performed.

Interpreting spoken requests

Systems and processes for processing natural language input are described. An example process for processing natural language input includes receiving a natural language input and determining a domain corresponding to the natural language input. The example process further includes, in accordance with determining the domain corresponding to the natural language input, determining, based on the natural language input, a first value for a first property of the domain and determining, based on a named entity model and the natural language input, a second value for the first property of the domain, where the second value defines a parameter for a task corresponding to the natural language input. The example process further includes performing the task based on the parameter and providing a result based on the performed task.

Named entity normalization in a spoken dialog system

Systems and processes for operating an intelligent automated assistant are provided. An example process receives natural language input and determines a first and a second parsing result for the natural language input. The first and the second parsing results include respective mappings of one or more properties of a domain corresponding to the natural language input to one or more words of the natural language input. The process determines whether the second parsing result corresponds to a data item in a knowledge base, and in accordance with determining that the second parsing result corresponds to the data item in the knowledge base, the process ranks the second parsing result higher than the first parsing result. Based on the ranking, the process generates a task flow using the second parsing result and executes the task flow to provide an output based on the data item.

Knowledge-based framework for improving natural language understanding

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Cupertino, CA, United States of America

Deepak Muralidharan