Adapting automated assistants for use with multiple languages

A language and auto-encoder technology, applied in natural language translation, natural language data processing, special data processing applications, etc., can solve problems such as difficulty in finding language experts

Pending Publication Date: 2021-09-17
GOOGLE LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition to this challenge, each language has myriad idiosyncrasies (e.g., morphologically rich, pre-forgettin...

Method used

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  • Adapting automated assistants for use with multiple languages
  • Adapting automated assistants for use with multiple languages
  • Adapting automated assistants for use with multiple languages

Examples

Experimental program
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Embodiment Construction

[0032] now turn figure 1 , illustrating an example environment in which the techniques disclosed herein may be implemented. The example environment includes multiple client computing devices 106 1-N . Each client device 106 may execute a corresponding instance of automated assistant client 118 . One or more cloud-based automated assistant components 119, such as natural language understanding engine 135, may be communicatively coupled to client device 106 via one or more local and / or wide area networks (e.g., the Internet), indicated generally at 110 1-N implemented on one or more computing systems (collectively referred to as "cloud" computing systems).

[0033] In some implementations, multiple client computing devices 106 1-N (also referred to herein simply as "client devices") may be associated with each other in various ways to facilitate performance of the techniques described herein. For example, in some implementations, multiple client computing devices ...

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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.

Description

Background technique [0001] Humans can engage in interactive software applications referred to herein as "automated assistants" (also referred to as "chat programs," "interactive personal assistants," "intelligent personal assistants," "personal voice assistants," "conversational agents," etc.) man-machine dialogue. For example, humans (who may be referred to as "users" when interacting with automated assistants) may use free-form natural language input and / or typed free-form natural language input to provide commands, queries, and / or requests (in (collectively referred to as "queries"), the free-form natural language input may be a vocal utterance that is converted to text and then processed. Automated assistants are typically invoked using predetermined voice utterances (e.g., "OK Assistant"), and typically perform various types of processing, such as speech-to-text processing and / or natural language processing. [0002] Automated assistants are good at communicating with...

Claims

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Application Information

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IPC IPC(8): G10L15/22G06F16/33G06F40/35G06F40/44G06F40/56G06F40/58
CPCG10L15/22G06F16/3337G06F40/35G06F40/44G06F40/56G06F40/58G06F40/51G06F40/30G10L15/063G10L19/008
Inventor 梅尔文·乔斯·约翰逊普雷姆库马尔弗拉迪米尔·武什科维奇詹姆斯·库茨马尔斯基柴宏杰
Owner GOOGLE LLC
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