公开(公告)号：US11942082B2

公开(公告)日：2024-03-26

申请号：US17825778

申请日：2022-05-26

Applicant: GOOGLE LLC

Inventor： James Kuczmarski ,  Vibhor Jain ,  Amarnag Subramanya ,  Nimesh Ranjan ,  Melvin Jose Johnson Premkumar ,  Vladimir Vuskovic ,  Luna Dai ,  Daisuke Ikeda ,  Nihal Sandeep Balani ,  Jinna Lei ,  Mengmeng Niu ,  Hongjie Chai ,  Wangqing Yuan

IPC: G06F40/47 ,  G06F16/33 ,  G06F16/332 ,  G06F18/22 ,  G06F40/58 ,  G06N20/00 ,  G10L15/00 ,  G10L15/183 ,  G10L15/22 ,  H04L51/02

CPC classification number: G10L15/183 ,  G06F16/3329 ,  G06F16/3337 ,  G06F18/22 ,  G06F40/47 ,  G06F40/58 ,  G06N20/00 ,  G10L15/005 ,  G10L15/22 ,  H04L51/02

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

公开(公告)号：US20240020491A1

公开(公告)日：2024-01-18

申请号：US18374071

申请日：2023-09-28

Applicant: Google LLC

Inventor： Zhifeng Chen ,  Macduff Richard Hughes ,  Yonghui Wu ,  Michael Schuster ,  Xu Chen ,  Llion Owen Jones ,  Niki J. Parmar ,  George Foster ,  Orhan Firat ,  Ankur Bapna ,  Wolfgang Macherey ,  Melvin Jose Johnson Premkumar

IPC: G06F40/58 ,  G06N3/08

CPC classification number: G06F40/58 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for machine translation using neural networks. In some implementations, a text in one language is translated into a second language using a neural network model. The model can include an encoder neural network comprising a plurality of bidirectional recurrent neural network layers. The encoding vectors are processed using a multi-headed attention module configured to generate multiple attention context vectors for each encoding vector. A decoder neural network generates a sequence of decoder output vectors using the attention context vectors. The decoder output vectors can represent distributions over various language elements of the second language, allowing a translation of the text into the second language to be determined based on the sequence of decoder output vectors.

公开(公告)号：US11875788B2

公开(公告)日：2024-01-16

申请号：US17211488

申请日：2021-03-24

Applicant: Google LLC

Inventor： James Kuczmarski ,  Vibhor Jain ,  Amarnag Subramanya ,  Nimesh Ranjan ,  Melvin Jose Johnson Premkumar ,  Vladimir Vuskovic ,  Luna Dai ,  Daisuke Ikeda ,  Nihal Sandeep Balani ,  Jinna Lei ,  Mengmeng Niu

IPC: G06F40/00 ,  G10L15/183 ,  G10L15/00 ,  G10L15/22 ,  G06F16/33 ,  G06N20/00 ,  G06F16/332 ,  G06F40/47 ,  G06F40/58 ,  H04L51/02 ,  G06F18/22

CPC classification number: G10L15/183 ,  G06F16/3329 ,  G06F16/3337 ,  G06F18/22 ,  G06F40/47 ,  G06F40/58 ,  G06N20/00 ,  G10L15/005 ,  G10L15/22 ,  H04L51/02

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

公开(公告)号：US11809834B2

公开(公告)日：2023-11-07

申请号：US17459041

申请日：2021-08-27

Applicant: Google LLC

Inventor： Zhifeng Chen ,  Macduff Richard Hughes ,  Yonghui Wu ,  Michael Schuster ,  Xu Chen ,  Llion Owen Jones ,  Niki J. Parmar ,  George Foster ,  Orhan Firat ,  Ankur Bapna ,  Wolfgang Macherey ,  Melvin Jose Johnson Premkumar

IPC: G06F40/58 ,  G06N3/08

CPC classification number: G06F40/58 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for machine translation using neural networks. In some implementations, a text in one language is translated into a second language using a neural network model. The model can include an encoder neural network comprising a plurality of bidirectional recurrent neural network layers. The encoding vectors are processed using a multi-headed attention module configured to generate multiple attention context vectors for each encoding vector. A decoder neural network generates a sequence of decoder output vectors using the attention context vectors. The decoder output vectors can represent distributions over various language elements of the second language, allowing a translation of the text into the second language to be determined based on the sequence of decoder output vectors.

公开(公告)号：US20220284198A1

公开(公告)日：2022-09-08

申请号：US17825778

申请日：2022-05-26

Applicant: GOOGLE LLC

Inventor： James Kuczmarski ,  Vibhor Jain ,  Amarnag Subramanya ,  Nimesh Ranjan ,  Melvin Jose Johnson Premkumar ,  Vladimir Vuskovic ,  Luna Dai ,  Daisuke Ikeda ,  Nihal Sandeep Balani ,  Jinna Lei ,  Mengmeng Niu ,  Hongjie Chai ,  Wangqing Yuan

IPC: G06F40/58 ,  G06F40/47 ,  G06F16/33 ,  G06K9/62 ,  H04L51/02 ,  G06N20/00 ,  G06F16/332

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

公开(公告)号：US20190258961A1

公开(公告)日：2019-08-22

申请号：US16402787

申请日：2019-05-03

Applicant: Google LLC

Inventor： Zhifeng Chen ,  Michael Schuster ,  Melvin Jose Johnson Premkumar ,  Yonghui Wu ,  Quoc V. Le ,  Maxim Krikun ,  Thorsten Brants

IPC: G06N20/00 ,  G06N3/04 ,  G06F17/28

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for performing machine learning tasks. One method includes receiving (i) a model input, and (ii) data identifying a first machine learning task to be performed on the model input to generate a first type of model output for the model input; augmenting the model input with an identifier for the first machine learning task to generate an augmented model input; and processing the augmented model input using a machine learning model. An exemplary system applying implicit bridging for machine learning tasks, as described in this specification, trains a machine learning model to perform certain types of machine learning tasks without requiring explicit training data for the certain types of machine learning tasks to be used during training.

公开(公告)号：US20250021889A1

公开(公告)日：2025-01-16

申请号：US18897967

申请日：2024-09-26

Applicant: Google LLC

Inventor： Zhifeng Chen ,  Michael Schuster ,  Melvin Jose Johnson Premkumar ,  Yonghui Wu ,  Quoc V. Le ,  Maxim Krikun ,  Thorsten Brants

IPC: G06N20/00 ,  G06F40/44 ,  G06F40/47 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for performing machine learning tasks. One method includes receiving (i) a model input, and (ii) data identifying a first machine learning task to be performed on the model input to generate a first type of model output for the model input; augmenting the model input with an identifier for the first machine learning task to generate an augmented model input; and processing the augmented model input using a machine learning model, wherein the machine learning model has been trained on training data to perform a plurality of machine learning tasks including the first machine learning task, and wherein the machine learning model has been configured through training to process the augmented model input to generate a machine learning model output of the first type for the model input.

公开(公告)号：US20240378441A1

公开(公告)日：2024-11-14

申请号：US18661447

申请日：2024-05-10

Applicant: Google LLC

Inventor： Slav Petrov ,  Yonghui Wu ,  Andrew M. Dai ,  David Richard So ,  Dmitry Lepikhin ,  Erica Ann Moreira ,  Gaurav Mishra ,  Jonathan Hudson Clark ,  Maxim Krikun ,  Melvin Jose Johnson Premkumar ,  Nan Du ,  Orhan Firat ,  Rohan Anil ,  Siamak Shakeri ,  Xavier Garcia ,  Yanping Huang ,  Yong Cheng ,  Yuanzhong Xu ,  Yujing Zhang ,  Zachary Alexander Nado ,  Eric Jun Jie Ni ,  Kefan Xiao ,  Vladimir Feinberg ,  Jin Young Sohn ,  Aurko Roy

IPC: G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform any one or more of a variety of machine learning tasks. For example, the neural network can be configured as a generative neural network, e.g., an autoregressive generative neural network.

公开(公告)号：US11138392B2

公开(公告)日：2021-10-05

申请号：US16521780

申请日：2019-07-25

Applicant: Google LLC

Inventor： Zhifeng Chen ,  Macduff Richard Hughes ,  Yonghui Wu ,  Michael Schuster ,  Xu Chen ,  Llion Owen Jones ,  Niki J. Parmar ,  George Foster ,  Orhan Firat ,  Ankur Bapna ,  Wolfgang Macherey ,  Melvin Jose Johnson Premkumar

IPC: G06F40/58 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for machine translation using neural networks. In some implementations, a text in one language is translated into a second language using a neural network model. The model can include an encoder neural network comprising a plurality of bidirectional recurrent neural network layers. The encoding vectors are processed using a multi-headed attention module configured to generate multiple attention context vectors for each encoding vector. A decoder neural network generates a sequence of decoder output vectors using the attention context vectors. The decoder output vectors can represent distributions over various language elements of the second language, allowing a translation of the text into the second language to be determined based on the sequence of decoder output vectors.

公开(公告)号：US20210210076A1

公开(公告)日：2021-07-08

申请号：US17211488

申请日：2021-03-24

Applicant: Google LLC

Inventor： James Kuczmarski ,  Vibhor Jain ,  Amarnag Subramanya ,  Nimesh Ranjan ,  Melvin Jose Johnson Premkumar ,  Vladimir Vuskovic ,  Luna Dai ,  Daisuke Ikeda ,  Nihal Sandeep Balani ,  Jinna Lei ,  Mengmeng Niu

IPC: G10L15/183 ,  G10L15/00 ,  G10L15/22

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.