Language Weaver, Inc.

University of Southern California

Educational Testing Service

Sdl Inc.

Daniel Marcu

Kevin Knight

Jill C Burstein

Radu Soricut

Narayanaswamy Viswanathan

Dragos Stefan Munteanu

Martin S Chodorow

William Wong

Philipp Koehn

Vyacheslav Andreyev

Claudia Leacock

Derrick Higgins

Markus Dreyer

Claudia Gentile

Felix Lung

Michel Galley

Kenji Yamada

Ignacio E Thayer

Jonathan May

Wei Wang

Greg Langmead

Ion Muslea

Mark Hopkins

Alexander Fraser

A prediction of the cost associated with translating textual content in a source language can be determined. A first quantity estimation of first textual content may be determined. The first textual content is to be translated via human translation. A second quantity estimation of second textual content may also be determined. The second textual content is to be translated via machine translation. An indication of a target language is obtained, wherein the source language and the target language form a language pair. The prediction of the cost associated with translating the first textual content and the second textual content from the source language to the target language is then determined. The prediction is based at least in part on the first quantity estimation, the second quantity estimation, and the language pair.

Systems and methods for translating textual content

Predicting the cost associated with translating textual content

The present invention provides a method that includes receiving a result word set in a target language representing a translation of a test word set in a source language. When the result word set is not in a set of acceptable translations, the method includes measuring a minimum number of edits to transform the result word set into a transform word set. The transform word set is in the set of acceptable translations. A system is provided that includes a receiver to receive a result word set and a counter to measure a minimum number of edits to transform the result word set into a transform word set. A method is provided that includes automatically determining a translation ability of a human translator based on a test result. The method also includes adjusting the translation ability of the human translator based on historical data of translations performed by the human translator.

Method and system for automatic management of reputation of translators

A learning system for a text-to-text application such as a machine translation system. The system has questions, and a matrix of correct answers to those questions. Any of the many different correct answers within the matrix can be considered as perfectly correct answers to the question. The system operates by displaying a question, which may be a phrase to be translated, and obtaining an answer to the question from the user. The answer is compared against the matrix and scored. Feedback may also be provided to the user.

Language capability assessment and training apparatus and techniques

Personalizing machine translation via online adaptation is described herein. According to some embodiments, methods for providing personalized machine translations may include receiving translator feedback regarding machine translations generated by a machine translation system for a translator, determining translator feedback that improves translations generated by the machine translation system, and incorporating the determined translator feedback into the translation methodology of the machine translation system to personalize the translation methodology.

Personalized machine translation via online adaptation

A document containing text in a source language may be translated into a target language based on content associated with that document, in conjunction with the present technology. An indication to perform an optimal translation of a document into a target language may be received via a user interface. The document may then be accessed by a computing device. The optimal translation is executed by a preferred translation engine of a plurality of available translation engines. The preferred translation engine is the most likely to produce the most accurate translation of the document among the plurality of available translation engines. Additionally, the preferred translation engine may be identified based on content associated with the document. The document is translated into the target language using the preferred translation engine to obtain a translated document, which may then be outputted by a computing device.

Translating documents based on content

Systems, computer programs, and methods for identifying parallel documents and/or fragments in a bilingual collection are provided. The method for identifying parallel sub-sentential fragments in a bilingual collection comprises translating a source document from a bilingual collection. The method further includes querying a target library associated with the bilingual collection using the translated source document, and identifying one or more target documents based on the query. Subsequently, a source sentence associated with the source document is aligned to one or more target sentences associated with the one or more target documents. Finally, the method includes determining whether a source fragment associated with the source sentence comprises a parallel translation of a target fragment associated with the one or more target sentences.

Systems and methods for identifying parallel documents and sentence fragments in multilingual document collections

A system and method for capitalizing translated text is provided. A capitalized source text is automatically translated to a target text. The target text is capitalized according to information in the capitalized source text.

System and method for capitalizing machine translated text

Systems and methods for generating trust scores for translations are described herein. According to some embodiments, methods for generating a trust score for a translation may include establishing a trust score for at least a portion of a first translation of a source text translated by a trusted translation system, the trust score representing an accuracy level for the first translation, comparing the first translation of the source text generated by the trusted translation system to a second translation of the source text generated by an untrusted translation system, and determining a trust score for the second translation based upon the comparison.

Trust scoring for language translation systems

Embodiments of the present invention provide a system and method for providing a translation service. The method comprises providing a translation interface accessible via a network. The translation interface receives specialized data associated with a domain from a member. A text string written in a source language is received from the member via the translation interface. A domain-based translation engine is selected. The domain-based translation engine may be associated with a source language, a target language, and a domain. The text string is translated into the target language using, at least in part, the selected domain-based translation engine. The translated text string is transmitted to the member via the Internet. In some embodiments, a translation memory is generated based on the specialized data.

Customizable machine translation service

Customers having a translation project to select a translation method from a variety of options, ranging from a completely human translation to a completely automated translation. For human translations, translation job information may be communicated through one or more network service modules which execute within a network service application, such as a web-based networking application. A network service module may register a user having an account with the network service application as a translator and communicate translation jobs to the user. One or more users who express interest in performing the translation are selected to perform a translation job, each job comprising at least a portion of the translation project. After a user provides a translation for the translation job, the translation is analyzed to generate a trust level prediction for the translation. A user translation profile may be updated after each translation to reflect the user's performance.

Providing human-generated and machine-generated trusted translations

To automatically annotate an essay, a sentence of the essay is identified and a feature associated with the sentence is determined. In addition, a probability of the sentence being a discourse element is determined by mapping the feature to a model. The model having been generated by a machine learning application based on at least one annotated essay. Furthermore, the essay is annotated based on the probability.

Automated annotation

A system, method, and computer program for generating and exploiting an approximate language model are provided. The method comprises generating a language model according to an approximate hashing technique. The language model comprises a plurality of event sequences in a target language, and each member of the plurality of the event sequences is associated with at least one count. The language model is queried for a member of the plurality of event sequences. A probability associated with the member of the plurality of event sequences is determined based on results of the query.

Generation and exploitation of an approximate language model

Training and translation using trees and/or subtrees as parts of the rules. A target language is word aligned with a source language, and at least one of the languages is parsed into trees. The trees are used for training, by aligning conversion steps, forming a manual set of information representing the conversion steps and then learning rules from that reduced set. The rules include subtrees as parts thereof, and are used for decoding, along with an n-gram language model and a syntax based language mode.

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Hermosa Beach, CA, United States of America

Daniel Marcu