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.

    Systems and methods for protecting against exposure to content violating a content policy

    公开(公告)号:US11582243B2

    公开(公告)日:2023-02-14

    申请号:US17066239

    申请日:2020-10-08

    Applicant: GOOGLE LLC

    Abstract: A method for protecting against exposure to content violating a content policy, the method including receiving a number of content items including a first set of content items associated with a content group, determining a measurement associated with an amount of the first set of content items belonging to a specific content category, assigning one or more of the number of content items to be categorized by at least one of the machine learning algorithm or a manual review process, automatically applying the specific content category to one or more other content items of the content group such that the one or more other content items are not reviewed by the manual review process, and transmitting at least one of the number of content items, wherein the content category of each of the number of content items indicates whether the specific content item violates any content policies.

    SYSTEMS AND METHODS FOR PROTECTING AGAINST EXPOSURE TO CONTENT VIOLATING A CONTENT POLICY

    公开(公告)号:US20220116402A1

    公开(公告)日:2022-04-14

    申请号:US17066239

    申请日:2020-10-08

    Applicant: GOOGLE LLC

    Abstract: A method for protecting against exposure to content violating a content policy, the method including receiving a number of content items including a first set of content items associated with a content group, determining a measurement associated with an amount of the first set of content items belonging to a specific content category, assigning one or more of the number of content items to be categorized by at least one of the machine learning algorithm or a manual review process, automatically applying the specific content category to one or more other content items of the content group such that the one or more other content items are not reviewed by the manual review process, and transmitting at least one of the number of content items, wherein the content category of each of the number of content items indicates whether the specific content item violates any content policies.

    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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