Abstract: Systems, methods, and media for generating analytical data from actions performed on one or more publishing servers. Methods may include capturing one or more audit trails by determining actions performed on the one or more publishing servers via one or more client devices, the one or more publishing servers adapted to publish informational content; generating analytical data from the one or more audit trails; and storing the generated analytical data in a database.

Abstract: Systems, methods, and media for controlling the review of documents. Methods may include receiving a request to review a document, responsive to the request, retrieving the document, the document including source content in an extensible markup language format, the document having a read-only access file permission, converting the document to read-write access file permission such that the source content is modifiable, receiving a modification of the source content of the document, incorporating the modification of the source content into the document to create a modified document, and automatically providing the modified document in a displayable format via the web-based interface.

Abstract: Systems and methods of machine learning for digital assets and message creation are provided herein. The present disclosure includes mechanisms for receiving one or more assets that include textual content, performing machine learning on the one or more assets in order to determine relevant words, phrases, and statistics included in the textual content, and displaying segments of data on a graphical user interface that also includes an interface that is used to create a message using content of the segments of the textual content that have been extracted from the one or more assets.

Abstract: A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

Abstract: A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

Abstract: Systems, methods, and media for generating analytical data from actions performed on one or more publishing servers. Methods may include capturing one or more audit trails by determining actions performed on the one or more publishing servers via one or more client devices, the one or more publishing servers adapted to publish informational content; generating analytical data from the one or more audit trails; and storing the generated analytical data in a database.

Abstract: Fragment recall and adaptive automated translation are disclosed herein. An example method includes determining that an exact or fuzzy match for a portion of a source input cannot be found in a translation memory, performing fragment recall by matching subsegments in the portion against one or more whole translation units stored in the translation memory, and matching subsegments in the portion against corresponding one or more subsegments inside the one or more matching whole translation units, and returning any of the one or more matching whole translation units and the one or more matching subsegments as a fuzzy match, as well as the translations of those subsegments.

Abstract: Systems, methods, and media for controlling the review of documents. Methods may include receiving a request to review a document, responsive to the request, retrieving the document, the document including source content in an extensible markup language format, the document having a read-only access file permission, converting the document to read-write access file permission such that the source content is modifiable, receiving a modification of the source content of the document, incorporating the modification of the source content into the document to create a modified document, and automatically providing the modified document in a displayable format via the web-based interface.

Abstract: Systems and methods of machine learning for digital assets and message creation are provided herein. The present disclosure includes mechanisms for receiving one or more assets that include textual content, performing machine learning on the one or more assets in order to determine relevant words, phrases, and statistics included in the textual content, and displaying segments of data on a graphical user interface that also includes an interface that is used to create a message using content of the segments of the textual content that have been extracted from the one or more assets.

Abstract: Systems and methods for determining a risk-reduced word price for editing. The editing can be of a document, for example, that has been translated by a human or a machine. A system and method may include providing to one or more preferred editors a first portion of editing jobs from a batch job. In some aspects, the system and method may include receiving an editing time of the one or more preferred editors editing the first portion of editing jobs. In further aspects, the system and method can include calculating a word price for editing a remaining portion of editing jobs of the batch job based on the editing time.

Abstract: 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.

Abstract: A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

Abstract: Fragment recall and adaptive automated translation are disclosed herein. An example method includes determining that an exact or fuzzy match for a portion of a source input cannot be found in a translation memory, performing fragment recall by matching subsegments in the portion against one or more whole translation units stored in the translation memory, and matching subsegments in the portion against corresponding one or more subsegments inside the one or more matching whole translation units, and returning any of the one or more matching whole translation units and the one or more matching subsegments as a fuzzy match, as well as the translations of those subsegments.

Abstract: Systems, methods, and media for controlling the review of documents. Methods may include receiving a request to review a document, responsive to the request, retrieving the document, the document including source content in an extensible markup language format, the document having a read-only access file permission, converting the document to read-write access file permission such that the source content is modifiable, receiving a modification of the source content of the document, incorporating the modification of the source content into the document to create a modified document, and automatically providing the modified document in a displayable format via the web-based interface.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods, systems and program product are disclosed for determining matching level of a text lookup segment with a plurality of source texts in a translation memory in terms of context. The invention determines exact matches for the lookup segment in the plurality of source texts, and determines, in the case that at least one exact match is determined, that a respective exact match is an in-context exact match for the lookup segment in the case that a context of the lookup segment matches that of the respective exact match. Degree of context matching required can be predetermined, and results prioritized. The invention also includes methods, systems and program products for storing a translation pair of source text and target text in a translation memory including context, and the translation memory so formed. The invention ensures that content is translated the same as previously translated content and reduces translator intervention.

Abstract: A system and method for translating data from a source language to a target language is provided wherein machine generated target translation of a source sentence is compared to a database of human generated target sentences. If a matching human generated target sentence is found, the human generated target sentence may be used instead of the machine generated sentence, since the human generated target sentence is more likely to be a well-formed sentence than the machine generated sentence. The system and method does not rely on a translation memory containing pairs of sentences in both source and target languages, and minimizes the reliance on a human translator to correct a translation generated by machine translation.