International Business Machines Corporation

Francis Xavier Rojas

Saroj K Vohra

Linda F Traudt

Alejandro Martinez Corria

Christophe D Chenon

Marc P Drapeau

Keiichi Yamamoto

Elizabeth Carol Meade

Santiago Pont Nesta

Consuelo Rodríguez Magro

Joseph C Ross

Jerald Lee Monson

Bianca Bonetto Leal Rodrigues

Patricia Pavan Esposito

Mauricio Kaoru Noriyuki

Almiro Jose Andrade, Jr

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.

Translation synthesizer for analysis, amplification and remediation of linguistic data across a translation supply chain

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.

Analytical optimization of translation and post editing

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.

Statistical process control and analytics for translation supply chain operational management

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. The integration and configuration of the system component within a translation supply chain assists in delivering a reliable level of translation quality by reducing the linguistic noise across all components of the supply chain.

Smart terminology marker system for a language translation system

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.

Measuring linguistic markers and linguistic noise of a machine-human translation supply chain

A mock translation system, method, and program is provided which converts single-byte base-language data and performs a mock translation on it to produce internationalization test data, which incorporates double-byte expansion characters having a second byte as &ldquo;5C&rdquo;, such as a double wide dash character. These expansion characters are used as a placeholder to accommodate the additional space needed for later translating the text into a different language. In addition, field boundary characters, such as brackets, are used to designate the beginning and end of the text with the placeholders. This data is stored in localization files and displayed in a software application in place of the English or foreign-language text. By visually inspecting each screen, the programmer or proofreader is able to easily recognize many internationalization errors, without requiring the ability to read any foreign language. These errors include truncation, alignment, or other formatting errors, and programming errors such as text that is hard-coded, localization files missing from the program build, text missing from localization files, and text composed of more than one translated message. Double-byte processing errors are also made evident if a back slash having a &ldquo;5C&rdquo; single-byte code appears in the displayed text in place of a double-wide dash character.

Mock translation system, method, and program using multi-byte placeholder characters to test translatability of software that will display multi-byte languages

A mock translation system, method and program is provided which converts single-byte base-language data and performs a mock translation on it to produce internationalization test data which takes the form of the corresponding base-language data transliterated into and displayed using a double-byte character set to create double-wide characters. The double-wide characters take into account the spacing, i.e., field length, needed to perform an actual translation. This data is stored in localization files and displayed in a software application in place of the English or foreign-language text. By visually inspecting each screen, the programmer or proofreader is able to easily recognize many internationalization errors, without requiring the ability to read any foreign languages. These errors include truncation, alignment, or other formatting errors, and programming errors such as text that is hard-coded, localization files missing from the program build, and text missing from localization files.

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Austin, TX, United States of America

Francis Xavier Rojas