Microsoft Technology Licensing, LLC

Microsoft Corporaiton

Microsoft Corporation

Arul A Menezes

Christopher Brian Quirk

Sharad Mathur

Robert C Moore

Stephen D Richardson

Colin A Cherry

Xuedong D Huang

Olivier Nano

Tanvi Surti

Ivo José Garcia Dos Santos

Anthony Aue

William Duncan Lewis

Christian Wendt

Richard F Rashid

William B Dolan

Eric David Brill

William Bachop Dolan

Michael Ginsberg

Lucretia Henrica Vanderwende

Michael Barnett

Michael R VanBuskirk

Franklin D Fite, Jr

Kevin Thomas Gallo

Jonas Lindblom

Eric K Ringger

Toby Lee Nixon

Kim A Stebbens

Pierre P N Greborio

Fredrik Furesjö

Brian Dean Haslam

Patrick Wayne John Evans

Patrick Santos

Hany M Hassan Awadalla

Steven D Richardson

Fredrik Furesjo

Pierre Pn Greborio

Jonas Nils Lindblom

Fredrik FuresjÖ

Pierre PN Greborio

Lucretia H Vanderwende

Systems and methods may be used to provide transcription and translation services. A method may include initializing a plurality of user devices with respective language output selections in a translation group by receiving a shared identifier from the plurality of user devices and transcribing the audio stream to transcribed text. The method may include translating the transcribed text to one or more of the respective language output selections when an original language of the transcribed text differs from the one or more of the respective language output selections. The method may include sending, a user device in the translation group, the transcribed text including translated text in a language corresponding to the respective language output selection for the user device. In an example, the method may include customizing the transcription or the translation, such as to a particular topic, location, user, or the like.

Customized transcribed conversations

Customized multi-device translated conversations

Customized multi-device translated and transcribed conversations

Technologies are described herein for cross-language speech recognition and translation. An example method of speech recognition and translation includes receiving an input utterance in a first language, the input utterance having at least one name of a named entity included therein and being pronounced in a second language, utilizing a customized language model to process at least a portion of the input utterance, and identifying the at least one name of the named entity from the input utterance utilizing a phonetic representation of the at least one name of the named entity. The phonetic representation has a pronunciation of the at least one name in the second language.

Cross-language speech recognition and translation

The disclosure pertains to a communication system for effecting a voice or video call between at least a source user speaking a source language and a target user speaking a target language. A translation procedure is performed on call audio of the call to generate an audio translation of the source user's speech in the target language for outputting to the target user. A notification is outputted to the target user to notify the target user of a change in the behavior of the translation procedure, the change relating to the generation of the translation.

In-call translation

The described implementations relate to processing of electronic data. One implementation is manifested as a system that can include a cache module and at least one processing device configured to execute the cache module. The cache module can be configured to store data items in slots of a cache structure, receive a request for an individual data item that maps to an individual slot of the cache structure, and, when the individual slot of the cache structure is not available, return without further processing the request. For example, the request can be received from a calling application or thread that can proceed without blocking irrespective of whether the request is fulfilled by the cache module.

Non-blocking caching technique

A method of translation includes uploading a source text portion to a back end processor. The back end processor identifies a subset of translation knowledge associated with the source text portion. The back end processor downloads the subset to a front end processor. A translation engine runs on the front end processor. The translation engine generates a translation of the source text portion as a function of the subset.

Machine translation split between front end and back end processors

A computer-implemented machine translation system translates text from a first language to a second language. The system includes a plurality of mappings, each mapping indicative of associating a dependency structure of the first language with a dependency structure of the second language, wherein at least some of the mappings correspond to dependency structures of the first language having varying context with some common elements, and associated dependency structures of the second language to the dependency structures of the first language. A module receives input text in a first language and outputs output text in a second language based on accessing the plurality of mappings.

Machine language translation with transfer mappings having varying context

Many machine translation scenarios involve the generation of a language translation rule set based on parallel training corpuses (e.g., sentences in a first language and word-for-word translations into a second language.) However, the translation of a source corpus in a source language to a target corpus in a target language involves at least two aspects: selecting elements of the target language to match the elements of the source corpus, and ordering the target elements according to the semantic organization of the source corpus and the grammatic rules of the target language. The breadth of generalization of the translation rules derived from the training may be improved, while retaining contextual information, by formulating language order templates that specify orderings of small sets of target elements according to target element types. These language order templates may be represented with varying degrees of association with the alignment rules derived from the training in order to improve the scope of target elements to which the ordering rules and alignment rules may be applied.

Machine translation using language order templates

In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.

Extracting treelet translation pairs

The present invention may be used to analyze subject content, search and analyze reference content, compare the subject and reference content for similarity, and output comparison reports between the subject and reference content. The present invention may incorporate and utilize text from intrinsic and/or extrinsic subject documents. The analysis may employ a variety of metrics, including scores generated from a natural language processing system, scores based on classification similarity, scores based on proximity similarity, and in the case of analysis of patent documents, scores based on measurement of claims.

Parsing, analysis and scoring of document content

A method of aligning nodes of dependency structures obtained from a bilingual corpus includes a two-phase approach wherein a first phase comprises associating nodes of the dependency structures to form tentative correspondences. The nodes of the dependency structures are then aligned as a function of the tentative correspondences and structural considerations. Mappings are obtained from the aligned dependency structures. The mappings can be expanded with varying types and amounts of local context in order that a more fluent translation can be obtained when translation is performed.

Automatic extraction of transfer mappings from bilingual corpora

Machine translation system incorporating syntactic dependency treelets into a statistical framework

A method of decoding an input semantic structure to generate an output semantic structure. A set of transfer mappings are provided. A score is calculated for at least one transfer mapping in the set of transfer mappings using a statistical model. At least one transfer mapping is selected based on the score and used to construct the output semantic structure.

Statistical language model for logical form using transfer mappings

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Sammamish, WA, United States of America

Arul A Menezes