Adapting automated assistants for use with multiple languages

    公开(公告)号:US11113481B2

    公开(公告)日:2021-09-07

    申请号:US16621578

    申请日:2019-05-02

    Applicant: GOOGLE LLC

    Abstract: Techniques described herein may serve to increase the language coverage of an automated assistant system, i.e. they may serve to increase the number of queries in one or more non-native languages for which the automated assistant is able to deliver reasonable responses. For example, techniques are described herein for training and utilizing a machine translation model to map a plurality of semantically-related natural language inputs in one language to one or more canonical translations in another language. In various implementations, the canonical translations may be selected and/or optimized for determining an intent of the speaker by the automated assistant, so that one or more responsive actions can be performed based on the speaker's intent. Put another way, the canonical translations may be specifically formatted for indicating the intent of the speaker to the automated assistant.

    ADAPTING AUTOMATED ASSISTANTS FOR USE WITH MULTIPLE LANGUAGES

    公开(公告)号:US20210064828A1

    公开(公告)日:2021-03-04

    申请号:US16621578

    申请日:2019-05-02

    Applicant: Google LLC

    Abstract: Techniques described herein may serve to increase the language coverage of an automated assistant system, i.e. they may serve to increase the number of queries in one or more non-native languages for which the automated assistant is able to deliver reasonable responses. For example, techniques are described herein for training and utilizing a machine translation model to map a plurality of semantically-related natural language inputs in one language to one or more canonical translations in another language. In various implementations, the canonical translations may be selected and/or optimized for determining an intent of the speaker by the automated assistant, so that one or more responsive actions can be performed based on the speaker's intent. Put another way, the canonical translations may be specifically formatted for indicating the intent of the speaker to the automated assistant.

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