公开(公告)号：US12032920B2

公开(公告)日：2024-07-09

申请号：US17056554

申请日：2020-03-07

申请人： Google LLC

发明人： Ye Jia ,  Zhifeng Chen ,  Yonghui Wu ,  Melvin Johnson ,  Fadi Biadsy ,  Ron Weiss ,  Wolfgang Macherey

IPC分类号： G06F40/47 ,  G06F40/58

CPC分类号： G06F40/47 ,  G06F40/58

摘要： The present disclosure provides systems and methods that train and use machine-learned models such as, for example, sequence-to-sequence models, to perform direct and text-free speech-to-speech translation. In particular, aspects of the present disclosure provide an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation.

公开(公告)号：US11138392B2

公开(公告)日：2021-10-05

申请号：US16521780

申请日：2019-07-25

申请人： Google LLC

发明人： 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分类号： G06F40/58 ,  G06N3/08

摘要： 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：US20200034436A1

公开(公告)日：2020-01-30

申请号：US16521780

申请日：2019-07-25

申请人： Google LLC

发明人： 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分类号： G06F17/28 ,  G06N3/08

摘要： 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：US11562152B2

公开(公告)日：2023-01-24

申请号：US17030093

申请日：2020-09-23

申请人： Google LLC

发明人： Naveen Arivazhagan ,  Colin Andrew Cherry ,  Wolfgang Macherey ,  Te I ,  George Foster ,  Pallavi N Baljekar

IPC分类号： G06F40/58 ,  G10L15/26 ,  G06F40/47 ,  G10L15/16 ,  G10L15/19

摘要： Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for re-translation for simultaneous, spoken-language machine translation. In some implementations, a stream of audio data comprising speech in a first language is received. A transcription for the speech in the stream of audio data is generated using an automated speech recognizer through a series of updates. A translation of the transcription into a second language is generated using a machine translation module. The translation is generated with translation iterations that translate increasing amounts of the transcription, including re-translating previously portions of the transcription. A series of translation updates are provided to a client device based on the translation iterations.

公开(公告)号：US20220092274A1

公开(公告)日：2022-03-24

申请号：US17030093

申请日：2020-09-23

申请人： Google LLC

发明人： Naveen Arivazhagan ,  Colin Andrew Cherry ,  Wolfgang Macherey ,  Te I ,  George Foster ,  Pallavi N. Baljekar

IPC分类号： G06F40/58 ,  G10L15/26 ,  G10L15/19 ,  G10L15/16 ,  G06F40/47

摘要： Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for re-translation for simultaneous, spoken-language machine translation. In some implementations, a stream of audio data comprising speech in a first language is received. A transcription for the speech in the stream of audio data is generated using an automated speech recognizer through a series of updates. A translation of the transcription into a second language is generated using a machine translation module. The translation is generated with translation iterations that translate increasing amounts of the transcription, including re-translating previously portions of the transcription. A series of translation updates are provided to a client device based on the translation iterations.

公开(公告)号：US20220083746A1

公开(公告)日：2022-03-17

申请号：US17459041

申请日：2021-08-27

申请人： Google LLC

发明人： 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分类号： G06F40/58 ,  G06N3/08

摘要： 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：US20210209315A1

公开(公告)日：2021-07-08

申请号：US17056554

申请日：2020-03-07

申请人： Google LLC

发明人： Ye Jia ,  Zhifeng Chen ,  Yonghui Wu ,  Melvin Johnson ,  Fadi Biadsy ,  Ron Weiss ,  Wolfgang Macherey

IPC分类号： G06F40/47 ,  G06F40/58

摘要： The present disclosure provides systems and methods that train and use machine-learned models such as, for example, sequence-to-sequence models, to perform direct and text-free speech-to-speech translation. In particular, aspects of the present disclosure provide an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation.

公开(公告)号：US20190325308A1

公开(公告)日：2019-10-24

申请号：US16458506

申请日：2019-07-01

申请人： Google LLC

发明人： Junyoung Chung ,  Melvin Jose Johnson Premkumar ,  Michael Schuster ,  Wolfgang Macherey

IPC分类号： G06N3/08 ,  G06N3/04 ,  G06F17/28

摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media for performing multi-task learning. In one method a system obtains a respective set of training data for each of multiple machine learning tasks. For each of the machine learning tasks, the system configures a respective teacher machine learning model to perform the machine learning task by training the teacher machine learning model on the training data. The system trains a single student machine learning model to perform the multiple machine learning tasks using (i) the configured teacher machine learning models, and (ii) the obtained training data.

公开(公告)号：US20240020491A1

公开(公告)日：2024-01-18

申请号：US18374071

申请日：2023-09-28

申请人： Google LLC

发明人： 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分类号： G06F40/58 ,  G06N3/08

CPC分类号： G06F40/58 ,  G06N3/08

摘要： 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：US11809834B2

公开(公告)日：2023-11-07

申请号：US17459041

申请日：2021-08-27

申请人： Google LLC

发明人： 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分类号： G06F40/58 ,  G06N3/08

CPC分类号： G06F40/58 ,  G06N3/08

摘要： 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.