Abstract: Automatic post-editing of machine translated content is disclosed herein. An example method includes presenting machine translated segments of a document and their associated quality estimation scores, invoking an automated post-editing system for segments with unsatisfactory translation quality, inputting the segments into a generative AI model alongside contextual information, producing a revised translation of the segment using the generative AI model and iterating the generative AI process with varying prompts until a quality estimation score of the machine translated segment meets or exceeds a quality estimation score threshold, or a predetermined number of attempts are reached.

Abstract: Systems and methods for generating analytical data based on captured audit trails are described. An exemplary method includes generating a natural language preference for the natural language of a document based on an opening action performed by an end user using a client device associated with the end user during a user session; generating a unique transaction key in response to the opening action; correlating subsequent actions relative to the document via the unique transaction key; generating content rich analytical data from an audit trail generated during the user session; obtaining informational content for the end user from informational content stored in a database; translating the obtained informational content; reformatting a native extensible markup language format of the obtained informational content obtained from the database; and providing the translated and formatted informational content to the end user.

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 and methods of machine learning for digital assets and message creation are provided herein. The present disclosure includes a method comprising means for receiving one or more assets that include textual content, means for performing machine learning on the one or more assets in order to determine relevant words, phrases, and statistics included in the textual content, and means for 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: Automatic post-editing of machine translated content is disclosed herein. An example method includes presenting a user with machine translated segments of a document and their associated quality estimation scores, allowing a user to invoke an automated post-editing system for segments with unsatisfactory translation quality, inputting the segments into a generative AI model alongside contextual information, producing a revised translation of the segment using the generative AI model and iterating the generative AI process with varying prompts until a satisfactory translation is achieved or a predetermined number of attempts are reached.

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.