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公开(公告)号：US09830404B2
公开(公告)日：2017-11-28
申请号：US14586074
申请日：2014-12-30
Applicant: Facebook, Inc.
Inventor： Fei Huang , Kay Rottmann , Ying Zhang , Matthias Gerhard Eck
IPC: G06F17/30
CPC classification number: G06F17/30991 , G06F17/30979
Abstract: Technology is discussed herein for identifying trending actions within a group of posts matching a query. A group of posts can be selected based on specified actions, action targets, or parameters such as author age, location, gender, when the posts were posted or what keywords they contain. Selected posts can be divided into sentences and a dependency structure can be created for each sentence classifying portions of the sentence as actions or action targets. Statistics can be generated for each sentence or post indicating whether it matches the actions, action targets, or other parameters specified in the query. Based on these statistics, additional information can be gathered to respond to questions posed in the query.
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公开(公告)号：US09747283B2
公开(公告)日：2017-08-29
申请号：US14981769
申请日：2015-12-28
Applicant: Facebook, Inc.
Inventor： Kay Rottmann , Fei Huang , Ying Zhang
CPC classification number: G06F17/2854 , G06F17/275 , G06F17/2809 , G06F17/289
Abstract: Technology is disclosed for snippet pre-translation and dynamic selection of translation systems. Pre-translation uses snippet attributes such as characteristics of a snippet author, snippet topics, snippet context, expected snippet viewers, etc., to predict how many translation requests for the snippet are likely to be received. An appropriate translator can be dynamically selected to produce a translation of a snippet either as a result of the snippet being selected for pre-translation or from another trigger, such as a user requesting a translation of the snippet. Different translators can generate high quality translations after a period of time or other translators can generate lower quality translations earlier. Dynamic selection of translators involves dynamically selecting machine or human translation, e.g., based on a quality of translation that is desired. Translations can be improved over time by employing better machine or human translators, such as when a snippet is identified as being more popular.
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公开(公告)号：US10114819B2
公开(公告)日：2018-10-30
申请号：US15192109
申请日：2016-06-24
Applicant: Facebook, Inc.
Inventor： Ying Zhang , Fei Huang , Kay Rottmann , Necip Fazil Ayan
Abstract: Exemplary embodiments relate to techniques for improving a machine translation system. The machine translation system may include one or more models for generating a translation. The system may generate multiple candidate translations, and may present the candidate translations to different groups of users, such as users of a social network. User engagement with the different candidate translations may be measured, and the system may determine which of the candidate translations was most favored by the users. For example, in the context of a social network, the number of times that the translation is liked or shared, or the number of comments associated with the translation, may be used to determine user engagement with the translation. The models of the machine translation system may be modified to favor the most-favored candidate translation. The translation system may repeat this process to continue to tune the models in a feedback loop.
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公开(公告)号：US10089299B2
公开(公告)日：2018-10-02
申请号：US15652144
申请日：2017-07-17
Applicant: Facebook, Inc.
Inventor： Kay Rottmann , Mirjam Maess
Abstract: Technology is disclosed that improves language processing engines by using multi-media (image, video, etc.) context data when training and applying language models. Multi-media context data can be obtained from one or more sources such as object/location/person identification in the multi-media, multi-media characteristics, labels or characteristics provided by an author of the multi-media, or information about the author of the multi-media. This context data can be used as additional input for a machine learning process that creates a model used in language processing. The resulting model can be used as part of various language processing engines such as a translation engine, correction engine, tagging engine, etc., by taking multi-media context/labeling for a content item as part of the input for computing results of the model.
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公开(公告)号：US20170371868A1
公开(公告)日：2017-12-28
申请号：US15192109
申请日：2016-06-24
Applicant: Facebook, Inc.
Inventor： Ying Zhang , Fei Huang , Kay Rottmann , Necip Fazil Ayan
CPC classification number: G06F17/2854 , G06F17/2818 , G06F17/289 , G06Q50/01
Abstract: Exemplary embodiments relate to techniques for improving a machine translation system. The machine translation system may include one or more models for generating a translation. The system may generate multiple candidate translations, and may present the candidate translations to different groups of users, such as users of a social network. User engagement with the different candidate translations may be measured, and the system may determine which of the candidate translations was most favored by the users. For example, in the context of a social network, the number of times that the translation is liked or shared, or the number of comments associated with the translation, may be used to determine user engagement with the translation. The models of the machine translation system may be modified to favor the most-favored candidate translation. The translation system may repeat this process to continue to tune the models in a feedback loop.
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Patent Agency Ranking
公开(公告)号：US09805029B2
公开(公告)日：2017-10-31
申请号：US14981794
申请日：2015-12-28
Applicant: Facebook, Inc.
Inventor： Kay Rottmann , Fei Huang , Ying Zhang
CPC classification number: G06F17/289 , G06F17/2854
Abstract: Technology is disclosed for snippet pre-translation and dynamic selection of translation systems. Pre-translation uses snippet attributes such as characteristics of a snippet author, snippet topics, snippet context, expected snippet viewers, etc., to predict how many translation requests for the snippet are likely to be received. An appropriate translator can be dynamically selected to produce a translation of a snippet either as a result of the snippet being selected for pre-translation or from another trigger, such as a user requesting a translation of the snippet. Different translators can generate high quality translations after a period of time or other translators can generate lower quality translations earlier. Dynamic selection of translators involves dynamically selecting machine or human translation, e.g., based on a quality of translation that is desired. Translations can be improved over time by employing better machine or human translators, such as when a snippet is identified as being more popular.
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公开(公告)号：US10733387B1
公开(公告)日：2020-08-04
申请号：US16447230
申请日：2019-06-20
Applicant: Facebook, Inc.
Inventor： Ying Zhang , Fei Huang , Kay Rottmann , Necip Fazil Ayan
Abstract: Exemplary embodiments relate to techniques for improving a machine translation system. The machine translation system may include one or more models for generating a translation. The system may generate multiple candidate translations, and may present the candidate translations to different groups of users, such as users of a social network. User engagement with the different candidate translations may be measured, and the system may determine which of the candidate translations was most favored by the users. For example, in the context of a social network, the number of times that the translation is liked or shared, or the number of comments associated with the translation, may be used to determine user engagement with the translation. The models of the machine translation system may be modified to favor the most-favored candidate translation. The translation system may repeat this process to continue to tune the models in a feedback loop.
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公开(公告)号：US20180373788A1
公开(公告)日：2018-12-27
申请号：US15821167
申请日：2017-11-22
Applicant: Facebook, Inc.
Inventor： Fei Huang , Kay Rottmann , Ying Zhang , Matthias Gerhard Eck
Abstract: Technology is discussed herein for identifying comparatively trending topics between groups of posts. Groups of posts can be selected based on parameters such as author age, location, gender, etc., or based on information about content items such as when they were posted or what keywords they contain. Topics, as one or more groups of words, can each be given a rank score for each group based on the topic's frequency within each group. A difference score for selected topics can be computed based on a difference between the rank score for the selected topic in each of the groups. When the difference score for a selected topic is above a specified threshold, that selected topic can be identified as a comparatively trending topic.
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公开(公告)号：US20180349515A1
公开(公告)日：2018-12-06
申请号：US15820351
申请日：2017-11-21
Applicant: Facebook, Inc.
Inventor： Fei Huang , Kay Rottmann , Ying Zhang , Matthias Gerhard Eck
IPC: G06F17/30
CPC classification number: G06F17/30991 , G06F17/30979
Abstract: Technology is discussed herein for identifying trending actions within a group of posts matching a query. A group of posts can be selected based on specified actions, action targets, or parameters such as author age, location, gender, when the posts were posted or what keywords they contain. Selected posts can be divided into sentences and a dependency structure can be created for each sentence classifying portions of the sentence as actions or action targets. Statistics can be generated for each sentence or post indicating whether it matches the actions, action targets, or other parameters specified in the query. Based on these statistics, additional information can be gathered to respond to questions posed in the query.
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公开(公告)号：US20170315991A1
公开(公告)日：2017-11-02
申请号：US15654668
申请日：2017-07-19
Applicant: Facebook, Inc.
Inventor： Kay Rottmann , Fei Huang , Ying Zhang
CPC classification number: G06F17/2854 , G06F17/275 , G06F17/2809 , G06F17/289
Abstract: Technology is disclosed for snippet pre-translation and dynamic selection of translation systems. Pre-translation uses snippet attributes such as characteristics of a snippet author, snippet topics, snippet context, expected snippet viewers, etc., to predict how many translation requests for the snippet are likely to be received. An appropriate translator can be dynamically selected to produce a translation of a snippet either as a result of the snippet being selected for pre-translation or from another trigger, such as a user requesting a translation of the snippet. Different translators can generate high quality translations after a period of time or other translators can generate lower quality translations earlier. Dynamic selection of translators involves dynamically selecting machine or human translation, e.g., based on a quality of translation that is desired. Translations can be improved over time by employing better machine or human translators, such as when a snippet is identified as being more popular.
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