-
公开(公告)号：US10692006B1
公开(公告)日：2020-06-23
申请号：US15199904
申请日：2016-06-30
Applicant: Facebook, Inc.
Inventor： Ying Zhang
Abstract: A chatbot can use a knowledge base including question/answer pairs to respond to questions. When a question is asked that does not correspond to a question/answer pair in the knowledge base, the chatbot can send the question to one or more humans to obtain an answer. However, only some people will have the experience, context, knowledge, etc., to answer the question. A model can be trained to select “experts” that are likely to be able to provide a good answer to a question by using both A) a vector comprising characteristics of questions and of the person posing the questions and B) a vector comprising characteristics of a possible expert. The model can trained to produce a value predicting how good an identified expert's answer is likely to be. The model can be trained based on measures of past answers provided for types of questions/questioners.
-
公开(公告)号：US10671816B1
公开(公告)日：2020-06-02
申请号：US15968983
申请日：2018-05-02
Applicant: Facebook, Inc.
Inventor： Ying Zhang , Fei Huang , Xiaolong Wang
IPC: G06F40/58 , G06F40/30 , G06F40/42 , G06F40/211
Abstract: Snippets can be represented in a language-independent semantic manner. Each portion of a snippet can be represented by a combination of a semantic representation and a syntactic representation, each in its own dimensional space. A snippet can be divided into portions by constructing a dependency structure based on relationships between words and phrases. Leaf nodes of the dependency structure can be assigned: A) a semantic representation according to pre-defined word mappings and B) a syntactic representation according to the grammatical use of the word. A trained semantic model can assign to each non-leaf node of the dependency structure a semantic representation based on a combination of the semantic and syntactic representations of the corresponding lower-level nodes. A trained syntactic model can assign to each non-leaf node a syntactic representation based on a combination of the syntactic representations of the corresponding lower-level nodes and the semantic representation of that node.
-
公开(公告)号：US10664664B2
公开(公告)日：2020-05-26
申请号：US15886817
申请日：2018-02-01
Applicant: Facebook, Inc.
Inventor： Ying Zhang , Fei Huang
IPC: G06F17/28 , G06F40/51 , G06F40/47 , G06F40/253 , G06F40/279
Abstract: A machine translation system can improve results of machine translations by employing preferred translations, such as human translated phrases. In some implementations, the machine translation system can use the preferred translations as heavily weighted training data when building a machine translation engine. In some implementations, the machine translation system can use the preferred translations as an alternate to a result that would have otherwise been produced by a machine translation engine. While it is infeasible to obtain human translations for all translation phrases, preferred translations can be used for problem phrases for which machine translation engines often produce poor translations. The machine translation system can identify problem phrases by assigning a quality score to each translation in a set of translations. The machine translation system can identify, as the problem phrases, n-grams that appear with a frequency above a frequency threshold in translations with quality scores below a threshold.
-
公开(公告)号：US10586168B2
公开(公告)日：2020-03-10
申请号：US14878762
申请日：2015-10-08
Applicant: Facebook, Inc.
Inventor： Ying Zhang , Fei Huang , Feng Liang
IPC: G06N20/00
Abstract: The described technology can provide semantic translations of a selected language snippet. This can be accomplished by mapping snippets for output languages into a vector space; creating predicates that can map new snippets into that vector space; and, when a new snippet is received, generating and matching a vector representing that new snippet to the closest vector for a snippet of a desired output language, which is used as the translation of the new snippet. The procedure for mapping new snippets into the vector space can include creating a dependency structure for the new snippet and computing a vector for each dependency structure node. The vector computed for the root node of the dependency structure is the vector representing the new snippet. A similar process is used to train a transformation function for each possible node type, using language snippets already associated with a dependency structure and corresponding vectors.
-
公开(公告)号：US20190197119A1
公开(公告)日：2019-06-27
申请号：US15850382
申请日：2017-12-21
Applicant: Facebook, Inc.
Inventor： Ying Zhang , Reshef Shilon , Jing Zheng
CPC classification number: G06F17/2845 , G06F17/2785 , G06F17/289
Abstract: Exemplary embodiments relate to techniques to classify or detect the intent of content written in a language for which a classifier does not exist. These techniques involve building a code-switching corpus via machine translation, generating a universal embedding for words in the code-switching corpus, training a classifier on the universal embeddings to generate an embedding mapping/table; accessing new content written in a language for which a specific classifier may not exist, and mapping entries in the embedding mapping/table to the universal embeddings. Using these techniques, a classifier can be applied to the universal embedding without needing to be trained on a particular language. Exemplary embodiments may be applied to recognize similarities in two content items, make recommendations, find similar documents, perform deduplication, and perform topic tagging for stories in foreign languages.
-
Patent Agency Ranking
36.发明授权公开(公告)号：US10255279B2
公开(公告)日：2019-04-09
申请号：US15864879
申请日：2018-01-08
Applicant: Facebook, Inc.
Inventor： Ying Zhang , Alexander Waibel
Abstract: A social networking system determines whether a particular user is qualified to provide translations of text from a first language to a second language. The determination may include evaluation of the language competencies of the user, and also of the trustworthiness of the user as a translator, as determined based on prior translations submitted by the user. The social networking system also selects translations of a text item for a user to whom that text is to be shown. When evaluating a candidate translation for presentation to the user, the evaluation may assess factors such as the determined qualification as a translator of the user who provided the candidate translation; a quality score of the candidate translation itself; and/or the similarity of the user viewing the content and the user providing the candidate translation.
-
公开(公告)号：US10255277B2
公开(公告)日：2019-04-09
申请号：US15192131
申请日：2016-06-24
Applicant: Facebook, Inc.
Inventor： Ying Zhang , Aram Grigoryan
Abstract: Exemplary embodiments relate to techniques for selecting translators willing to provide high-quality translations for a cause, organization, or individual. Users having a high level of engagement with the cause, organization, or individual may be identified as translator candidates. For example, the user may actively engage with the organization or individual on social media, or may be interested in the topics discussed in the source document. The translators may be evaluated based on the quality of their previous translations and their level of engagement/interest. The translator candidates may be directly connected with the originator of the request to translate the document. Because exemplary embodiments select highly engaged users to translate the source document, the resulting translation is likely to be of higher quality, and produced at a lower cost, than a translation by a non-engaged user, and user participation and awareness of a cause, organization, or individual may be increased.
-
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.
-
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.
-
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.
-
-
-
-
-
-
-
-
-
Search Results
71
records
(took 0.02 seconds)
