公开(公告)号：US20200034436A1

公开(公告)日：2020-01-30

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: G06F17/28 ,  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：US12217017B2

公开(公告)日：2025-02-04

申请号：US17791409

申请日：2020-01-08

Applicant: GOOGLE LLC

Inventor： Puneet Jain ,  Orhan Firat ,  Sihang Liang

IPC: G06F40/58 ,  G06V10/44 ,  G06V10/77 ,  G06V10/82 ,  G06V20/62

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that translate text depicted in images from a source language into a target language. Methods can include obtaining a first image that depicts first text written in a source language. The first image is input into an image translation model, which includes a feature extractor and a decoder. The feature extractor accepts the first image as input and in response, generates a first set of image features that are a description of a portion of the first image in which the text is depicted is obtained. The first set of image features are input into a decoder. In response to the input first set of image features, the decoder outputs a second text that is a predicted translation of text in the source language that is represented by the first set of image features.

公开(公告)号：US12210848B2

公开(公告)日：2025-01-28

申请号：US17682282

申请日：2022-02-28

Applicant: Google LLC

Inventor： Xavier Eduardo Garcia ,  Orhan Firat ,  Noah Constant ,  Xiaoyue Guo ,  Parker Riley

IPC: G06F40/58 ,  G06F40/166 ,  G06F40/197 ,  G06F40/253 ,  G06F40/56 ,  G06N3/045 ,  G06N3/047 ,  G06N3/08 ,  G06N3/084

Abstract: The technology provides a model-based approach for multilingual text rewriting that is applicable across many languages and across different styles including formality levels or other textual attributes. The model is configured to manipulate both language and textual attributes jointly. This approach supports zero-shot formality-sensitive translation, with no labeled data in the target language. An encoder-decoder architectural approach with attribute extraction is used to train rewriter models that can thus be used in “universal” textual rewriting across many different languages. A cross-lingual learning signal can be incorporated into the training approach. Certain training processes do not employ any exemplars. This approach enables not just straight translation, but also the ability to create new sentences with different attributes.

公开(公告)号：US11373049B2

公开(公告)日：2022-06-28

申请号：US16610233

申请日：2019-08-26

Applicant: Google LLC

Inventor： Melvin Jose Johnson Premkumar ,  Akiko Eriguchi ,  Orhan Firat

IPC: G06F40/58

Abstract: Training and/or using a multilingual classification neural network model to perform a natural language processing classification task, where the model reuses an encoder portion of a multilingual neural machine translation model. In a variety of implementations, a client device can generate a natural language data stream from a spoken input from a user. The natural language data stream can be applied as input to an encoder portion of the multilingual classification model. The output generated by the encoder portion can be applied as input to a classifier portion of the multilingual classification model. The classifier portion can generate a predicted classification label of the natural language data stream. In many implementations, an output can be generated based on the predicted classification label, and a client device can present the output.

公开(公告)号：US11341340B2

公开(公告)日：2022-05-24

申请号：US16590309

申请日：2019-10-01

Applicant: Google LLC

Inventor： Ankur Bapna ,  Ye Tian ,  Orhan Firat

IPC: G06F40/58 ,  G06F17/16 ,  G06K9/62

Abstract: Adapters for neural machine translation systems. A method includes determining a set of similar n-grams that are similar to a source n-gram, and each similar n-gram and the source n-gram is in a first language; determining, for each n-gram in the set of similar n-grams, a target n-gram is a translation of the similar n-gram in the first language to the target n-gram in the second language; generating a source encoding of the source n-gram, and, for each target n-gram determined from the set of similar n-grams determined for the source n-gram, a target encoding of the target n-gram and a conditional source target memory that is an encoding of each of the target encodings; providing, as input to a first prediction model, the source encoding and the condition source target memory; and generating a predicted translation of the source n-gram from the first language to the second language.

公开(公告)号：US20220083746A1

公开(公告)日：2022-03-17

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

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：US20240378427A1

公开(公告)日：2024-11-14

申请号：US18661499

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: G06N3/0475 ,  G06F40/284

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：US20240256966A1

公开(公告)日：2024-08-01

申请号：US18424660

申请日：2024-01-26

Applicant: Google LLC

Inventor： Ankush Garg ,  Yichi Zhang ,  Yuan Cao ,  Lukasz Lew ,  Orhan Firat ,  Behrooz Ghorbani

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing sequence generation tasks using binarized neural networks. The binarized neural network is an attention neural network configured to perform the task and the attention neural network includes a plurality of attention blocks, with each block including an attention block and a binarized feedforward block.

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：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.