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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
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.
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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
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.
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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
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.
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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
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.
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