公开(公告)号：US20230419053A1

公开(公告)日：2023-12-28

申请号：US17988315

申请日：2022-11-16

Applicant: Google LLC

Inventor： Jing Huang ,  Apurva Shah ,  Melvin Johnson ,  Viresh Ratnakar ,  Maxim Krikun

IPC: G06F40/58 ,  G06F40/51 ,  G06F40/49

CPC classification number: G06F40/58 ,  G06F40/51 ,  G06F40/49

Abstract: Systems and methods for training a translation model based on a first text sequence in a first language, a second text sequence in a second language different from the first language, and a label based on a source of the second text sequence. In some examples, the label may comprise an Internet domain, an Internet subdomain, a uniform resource locator, a website name, or an IP address. In some examples, the label may further indicate a source of the first text sequence. In some examples, each given training example may be automatically generated by sampling the first text sequence from a first page of a given Internet domain, sampling the second text sequence from a second page of the given Internet domain, and generating the label based on all or a portion of source data of the second page.

公开(公告)号：US20230252245A1

公开(公告)日：2023-08-10

申请号：US18015551

申请日：2020-08-07

Applicant: Google LLC

Inventor： Melvin Johnson

IPC: G06F40/51 ,  G06F40/47

CPC classification number: G06F40/51 ,  G06F40/47

Abstract: Generally, the present disclosure is directed to systems and methods that leverage machine learning to perform post-editing of sentence-level translations that takes into account contextual information from the language source. As an example, the proposed post-editing system can run as a second pass to a sentence-level translation system and the goal of the post-editing system may be to refine translations which are affected by the larger context.

公开(公告)号：US20210209315A1

公开(公告)日：2021-07-08

申请号：US17056554

申请日：2020-03-07

Applicant: Google LLC

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

IPC: G06F40/47 ,  G06F40/58

Abstract: 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：US12032920B2

公开(公告)日：2024-07-09

申请号：US17056554

申请日：2020-03-07

Applicant: Google LLC

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

IPC: G06F40/47 ,  G06F40/58

CPC classification number: G06F40/47 ,  G06F40/58

Abstract: 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.