Abstract: Systems and methods for translating a source segment are disclosed. In embodiments, a computer-implemented method for translating a source segment comprises receiving, by a computing device, the source segment in a first language to be translated into a second language; identifying, by the computing device, linguistic markers within the source segment and associated noise values to produce a tagged source segment, wherein the linguistic markers are associated with one or more linguistic patterns likely to introduce noise into a translation channel; transforming, by the computing device, the tagged source segment into an amplified source segment; and sending, by the computing device, the amplified source segment to a machine translation module, wherein the machine translation module is configured to process the amplified source segment to produce a return amplified match in the second language.

Abstract: Improved translation operations are disclosed. A first segment of text in a first language is received, to be translated into a second language. The first segment is evaluated, by operation of one or more processors, of text using the pattern model to generate a first s-score. A second segment of text in the second language is generated based on processing the first segment of text using a machine translation model. Upon determining that the first s-score exceeds a predefined threshold, the first segment of text is flagged for auto-substitution with the second segment of text, such that the first segment of text is not reviewed by a human editor.

Abstract: Improved translation operations are disclosed. A first segment of text in a first language is received, to be translated into a second language. The first segment is evaluated, by operation of one or more processors, of text using the pattern model to generate a first s-score. A second segment of text in the second language is generated based on processing the first segment of text using a machine translation model. Upon determining that the first s-score exceeds a predefined threshold, the first segment of text is flagged for auto-substitution with the second segment of text, such that the first segment of text is not reviewed by a human editor.

Abstract: Systems and methods for translating a source segment are disclosed. In embodiments, a computer-implemented method for translating a source segment comprises receiving, by a computing device, the source segment in a first language to be translated into a second language; identifying, by the computing device, linguistic markers within the source segment and associated noise values to produce a tagged source segment, wherein the linguistic markers are associated with one or more linguistic patterns likely to introduce noise into a translation channel; transforming, by the computing device, the tagged source segment into an amplified source segment; and sending, by the computing device, the amplified source segment to a machine translation module, wherein the machine translation module is configured to process the amplified source segment to produce a return amplified match in the second language.

Abstract: A method for translation supply chain analytics includes receiving operational variables of a translation process from a translation supply chain. The method further includes determining a cognitive leverage and a productivity factor for post editing of matches of a plurality of match types generated by the translation supply chain based at least in part on the operational variables from the translation supply chain. The method further includes generating linguistic markers for the matches of the plurality of match types generated by the translation supply chain, based at least in part on the cognitive leverage and the productivity factor for the post editing of the matches of the plurality of match types. The method further includes performing statistical analysis of the linguistic markers for the matches of the plurality of match types. The method further includes generating one or more analytics outputs based on the statistical analysis of the linguistic markers.

Abstract: A method for translation supply chain analytics includes receiving operational variables of a translation process from a translation supply chain. The method further includes determining a cognitive leverage and a productivity factor for post editing of matches of a plurality of match types generated by the translation supply chain based at least in part on the operational variables from the translation supply chain. The method further includes generating linguistic markers for the matches of the plurality of match types generated by the translation supply chain, based at least in part on the cognitive leverage and the productivity factor for the post editing of the matches of the plurality of match types. The method further includes performing statistical analysis of the linguistic markers for the matches of the plurality of match types. The method further includes generating one or more analytics outputs based on the statistical analysis of the linguistic markers.

Abstract: Systems and methods for translating a source segment are disclosed. In embodiments, a computer-implemented method for translating a source segment comprises receiving, by a computing device, the source segment in a first language to be translated into a second language; identifying, by the computing device, linguistic markers within the source segment and associated noise values to produce a tagged source segment, wherein the linguistic markers are associated with one or more linguistic patterns likely to introduce noise into a translation channel; transforming, by the computing device, the tagged source segment into an amplified source segment; and sending, by the computing device, the amplified source segment to a machine translation module, wherein the machine translation module is configured to process the amplified source segment to produce a return amplified match in the second language.

Abstract: Systems and methods for translating a source segment are disclosed. In embodiments, a computer-implemented method for translating a source segment comprises receiving, by a computing device, the source segment in a first language to be translated into a second language; identifying, by the computing device, linguistic markers within the source segment and associated noise values to produce a tagged source segment, wherein the linguistic markers are associated with one or more linguistic patterns likely to introduce noise into a translation channel; transforming, by the computing device, the tagged source segment into an amplified source segment; and sending, by the computing device, the amplified source segment to a machine translation module, wherein the machine translation module is configured to process the amplified source segment to produce a return amplified match in the second language.

Abstract: A terminology marker system integrates a terminology analytical component for quantifying the amount of linguistic noise found in the translation output as measured against a dictionary; further, correlating the noise measured on a continuous basis enables the analytical component to build terminology predictive models used in a feedback loop to upstream components of the supply chain to improve future translation of new content. The system also provides a smart terminology assessment component for assessing linguistic assets and improving the quality of those assets to assist in translation. The system also provides a smart terminology evaluation component that is able to analyze MT output to make smart decisions on reducing the amount of post editing corrections needed for delivering a persistent level of translation quality.

Abstract: Systems and methods for translating a source segment are disclosed. In embodiments, a computer-implemented method for translating a source segment comprises receiving, by a computing device, the source segment in a first language to be translated into a second language; identifying, by the computing device, linguistic markers within the source segment and associated noise values to produce a tagged source segment, wherein the linguistic markers are associated with one or more linguistic patterns likely to introduce noise into a translation channel; transforming, by the computing device, the tagged source segment into an amplified source segment; and sending, by the computing device, the amplified source segment to a machine translation module, wherein the machine translation module is configured to process the amplified source segment to produce a return amplified match in the second language.

Abstract: A terminology marker system integrates a terminology analytical component for quantifying the amount of linguistic noise found in the translation output as measured against a dictionary; further, correlating the noise measured on a continuous basis enables the analytical component to build terminology predictive models used in a feedback loop to upstream components of the supply chain to improve future translation of new content. The system also provides a smart terminology assessment component for assessing linguistic assets and improving the quality of those assets to assist in translation. The system also provides a smart terminology evaluation component that is able to analyze MT output to make smart decisions on reducing the amount of post editing corrections needed for delivering a persistent level of translation quality.

Abstract: A terminology marker system integrates a terminology analytical component for quantifying the amount of linguistic noise found in the translation output as measured against a dictionary; further, correlating the noise measured on a continuous basis enables the analytical component to build terminology predictive models used in a feedback loop to upstream components of the supply chain to improve future translation of new content. The system also provides a smart terminology assessment component for assessing linguistic assets and improving the quality of those assets to assist in translation. The system also provides a smart terminology evaluation component that is able to analyze MT output to make smart decisions on reducing the amount of post editing corrections needed for delivering a persistent level of translation quality.

Abstract: A terminology marker system integrates a terminology analytical component for quantifying the amount of linguistic noise found in the translation output as measured against a dictionary; further, correlating the noise measured on a continuous basis enables the analytical component to build terminology predictive models used in a feedback loop to upstream components of the supply chain to improve future translation of new content. The system also provides a smart terminology assessment component for assessing linguistic assets and improving the quality of those assets to assist in translation. The system also provides a smart terminology evaluation component that is able to analyze MT output to make smart decisions on reducing the amount of post editing corrections needed for delivering a persistent level of translation quality.

Abstract: A method for translation supply chain analytics includes receiving operational variables of a translation process from a translation supply chain. The method further includes determining a cognitive leverage and a productivity factor for post editing of matches of a plurality of match types generated by the translation supply chain based at least in part on the operational variables from the translation supply chain. The method further includes generating linguistic markers for the matches of the plurality of match types generated by the translation supply chain, based at least in part on the cognitive leverage and the productivity factor for the post editing of the matches of the plurality of match types. The method further includes performing statistical analysis of the linguistic markers for the matches of the plurality of match types. The method further includes generating one or more analytics outputs based on the statistical analysis of the linguistic markers.

Abstract: A terminology marker system integrates a terminology analytical component for quantifying the amount of linguistic noise found in the translation output as measured against a dictionary; further, correlating the noise measured on a continuous basis enables the analytical component to build terminology predictive models used in a feedback loop to upstream components of the supply chain to improve future translation of new content. The system also provides a smart terminology assessment component for assessing linguistic assets and improving the quality of those assets to assist in translation. The system also provides a smart terminology evaluation component that is able to analyze MT output to make smart decisions on reducing the amount of post editing corrections needed for delivering a persistent level of translation quality.

Abstract: A method for translation supply chain analytics includes receiving operational variables of a translation process from a translation supply chain. The method further includes determining a cognitive leverage and a productivity factor for post editing of matches of a plurality of match types generated by the translation supply chain based at least in part on the operational variables from the translation supply chain. The method further includes generating linguistic markers for the matches of the plurality of match types generated by the translation supply chain, based at least in part on the cognitive leverage and the productivity factor for the post editing of the matches of the plurality of match types. The method further includes performing statistical analysis of the linguistic markers for the matches of the plurality of match types. The method further includes generating one or more analytics outputs based on the statistical analysis of the linguistic markers.

Abstract: A method for translation supply chain analytics includes receiving operational variables of a translation process from a translation supply chain. The method further includes determining a cognitive leverage and a productivity factor for post editing of matches of a plurality of match types generated by the translation supply chain based at least in part on the operational variables from the translation supply chain. The method further includes generating linguistic markers for the matches of the plurality of match types generated by the translation supply chain, based at least in part on the cognitive leverage and the productivity factor for the post editing of the matches of the plurality of match types. The method further includes performing statistical analysis of the linguistic markers for the matches of the plurality of match types. The method further includes generating one or more analytics outputs based on the statistical analysis of the linguistic markers.

Abstract: An approach is provided in which a linguistic analyzer engine generates a leverage value of a language translation supply chain that corresponds to an amount of suggested translations that are accepted by a professional linguist. The linguistic analyzer engine generates a factor value of the language translation supply chain that indicates a productivity of the user to convert the set of accepted translation into a set of final translations. In turn, the linguistic analyzer engine determines a performance efficiency of the language translation supply chain based upon the generated leverage value and the generated factor value, and evaluates the language translation supply chain accordingly. In one embodiment, the linguistic analyzer engine determines a performance efficiency of the language translation supply chain based on “n” distinct metric values associated with final translated segments.

Abstract: An approach is provided in which a linguistic analyzer engine generates a leverage value of a language translation supply chain that corresponds to an amount of suggested translations that are accepted by a professional linguist. The linguistic analyzer engine generates a factor value of the language translation supply chain that indicates a productivity of the user to convert the set of accepted translation into a set of final translations. In turn, the linguistic analyzer engine determines a performance efficiency of the language translation supply chain based upon the generated leverage value and the generated factor value, and evaluates the language translation supply chain accordingly. In one embodiment, the linguistic analyzer engine determines a performance efficiency of the language translation supply chain based on “n” distinct metric values associated with final translated segments.

Abstract: According to one embodiment of the present invention, a method for automating content localization workflow including translation processes. The method includes generating initial metadata relating to the status of a content localization workflow. The current status of the metadata is determined and displayed on a user interface dashboard. The user interface dashboard may be used to modify the content localization workflow. The metadata is used to drive changes and interaction from one phase of the workflow to another in an automated manner.