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公开(公告)号：US10108611B1
公开(公告)日：2018-10-23
申请号：US15274781
申请日：2016-09-23
Applicant: Amazon Technologies, Inc.
IPC: G06F17/28
Abstract: A machine translation system capable of incremental and preemptive machine translation is disclosed. Content items on a page can be provided to multiple machine translation services for translation. Each of the machine translation services is capable of translating content items at a different quality level. Content items translated at a lower quality level might be received before content items translated at a higher quality level and presented in a user interface (UI). When content items translated at a higher quality level are received, the translated content items can be used to replace the lower quality level translations previously presented in the UI. Content items referenced by pages identified in search results can also be preemptively machine translated, cached, and provided when requests are received for the translated content items.
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Patent Agency Ranking
公开(公告)号：US10185713B1
公开(公告)日：2019-01-22
申请号：US14867932
申请日：2015-09-28
Applicant: Amazon Technologies, Inc.
Inventor： Michael Denkowski , Alon Lavie , Gregory Alan Hanneman , Austin Matthews , Matthew Ryan Fiorillo , Robert Thomas Olszewski , Christopher James Dyer , William Joseph Kaper , Alexandre Alexandrovich Klementiev , Gavin R. Jewell
Abstract: Technologies are disclosed herein for statistical machine translation. In particular, the disclosed technologies include extensions to conventional machine translation pipelines: the use of multiple domain-specific and non-domain-specific dynamic language translation models and language models; cluster-based language models; and large-scale discriminative training. Incremental update technologies are also disclosed for use in updating a machine translation system in four areas: word alignment; translation modeling; language modeling; and parameter estimation. A mechanism is also disclosed for training and utilizing a runtime machine translation quality classifier for estimating the quality of machine translations without the benefit of reference translations. The runtime machine translation quality classifier is generated in a manner to offset imbalances in the number of training instances in various classes, and to assign a greater penalty to the misclassification of lower-quality translations as higher-quality translations than to misclassification of higher-quality translations as lower-quality translations.
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3.发明授权公开(公告)号：US10679015B1
公开(公告)日：2020-06-09
申请号：US14981536
申请日：2015-12-28
Applicant: Amazon Technologies, Inc.
Inventor： Gyorgy Istvan Szarvas , Gregory Alan Hanneman , Alon Lavie
Abstract: Technologies are disclosed for utilizing artificial intelligence-based machine translation to augment document summarization. Text can be extracted from a document in a first language. Machine translation can be utilized to translate the text from the first language to a second language. The translated text can be used to identify documents in the second language that include support for the translated text. A user interface can be provided that indicates the number of documents in the second language that provide support for the extracted text. Documents in the first language can also be translated to the second language. Documents that provide support for a text string can be identified in the documents translated to the second language and in other documents in the second language. A user interface can be provided that indicates the number of documents in the first language and the second language that provide support for the text.
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公开(公告)号：US10268684B1
公开(公告)日：2019-04-23
申请号：US14868166
申请日：2015-09-28
Applicant: Amazon Technologies, Inc.
Inventor： Michael Denkowski , Alon Lavie , Gregory Alan Hanneman , Matthew Ryan Fiorillo , Laura Josephine Kieras , Robert Thomas Olszewski , William Joseph Kaper , Alexandre Alexandrovich Klementiev , Gavin Richard Jewell
Abstract: Technologies are disclosed herein for statistical machine translation. In particular, the disclosed technologies include extensions to conventional machine translation pipelines: the use of multiple domain-specific and non-domain-specific dynamic language translation models and language models; cluster-based language models; and large-scale discriminative training. Incremental update technologies are also disclosed for use in updating a machine translation system in four areas: word alignment; translation modeling; language modeling; and parameter estimation. A mechanism is also disclosed for training and utilizing a runtime machine translation quality classifier for estimating the quality of machine translations without the benefit of reference translations. The runtime machine translation quality classifier is generated in a manner to offset imbalances in the number of training instances in various classes, and to assign a greater penalty to the misclassification of lower-quality translations as higher-quality translations than to misclassification of higher-quality translations as lower-quality translations.
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公开(公告)号：US10108610B1
公开(公告)日：2018-10-23
申请号：US15274562
申请日：2016-09-23
Applicant: Amazon Technologies, Inc.
IPC: G06F17/28
Abstract: A machine translation system capable of incremental and preemptive machine translation is disclosed. Content items on a page can be provided to multiple machine translation services for translation. Each of the machine translation services is capable of translating content items at a different quality level. Content items translated at a lower quality level might be received before content items translated at a higher quality level and presented in a user interface (UI). When content items translated at a higher quality level are received, the translated content items can be used to replace the lower quality level translations previously presented in the UI. Content items referenced by pages identified in search results can also be preemptively machine translated, cached, and provided when requests are received for the translated content items.
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公开(公告)号：US09959271B1
公开(公告)日：2018-05-01
申请号：US14868083
申请日：2015-09-28
Applicant: Amazon Technologies, Inc.
Inventor： Kartik Goyal , Alon Lavie , Michael Denkowski , Gregory Alan Hanneman , Matthew Ryan Fiorillo , Robert Thomas Olszewski , Ehud Hershkovich , William Joseph Kaper , Alexandre Alexandrovich Klementiev , Gavin R. Jewell
CPC classification number: G06F17/2818 , G06F17/2854
Abstract: Technologies are disclosed herein for statistical machine translation. In particular, the disclosed technologies include extensions to conventional machine translation pipelines: the use of multiple domain-specific and non-domain-specific dynamic language translation models and language models; cluster-based language models; and large-scale discriminative training. Incremental update technologies are also disclosed for use in updating a machine translation system in four areas: word alignment; translation modeling; language modeling; and parameter estimation. A mechanism is also disclosed for training and utilizing a runtime machine translation quality classifier for estimating the quality of machine translations without the benefit of reference translations. The runtime machine translation quality classifier is generated in a manner to offset imbalances in the number of training instances in various classes, and to assign a greater penalty to the misclassification of lower-quality translations as higher-quality translations than to misclassification of higher-quality translations as lower-quality translations.
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