Translation Supply Chain Analytics for Post-Editing Productivity
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Solution Overview
Problem
Machine translation systems face inefficiencies in conveying the complexities of human languages, leading to suboptimal translation quality, which professional human translators must correct, resulting in increased labor and costs within translation supply chains.
Innovation Solution
A computing system and method that combines machine translation with human translators, utilizing statistical process analytics and control to analyze translation memory, machine translation of exact and fuzzy matches, and post-editing productivity, generating linguistic markers and analytics to identify inefficiencies and improve machine translation capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine translation is used to translate between natural languages, then translation speed and productivity are improved, but translation quality and accuracy deteriorate due to language idiosyncrasies and subtleties
Solution Approach 1:
The patent combines machine translation systems with human translator review and editing processes into an integrated translation supply chain. Machine translation handles initial translation at high speed, while human translators perform quality control and refinement, merging the productivity advantages of automation with the quality assurance of human expertise.
Solution Approach 2:
The patent implements feedback mechanisms where translation results are analyzed and used to improve future translations. Statistical process control analytics examine translation outcomes, identify errors and patterns, and feed this information back to refine machine translation models and guide human translator efforts, continuously improving both speed and quality over time.
2Manufacturing precision
If human translators manually translate content to ensure quality, then translation accuracy is improved, but labor costs and time consumption increase
Solution Approach 1:
The patent applies partial automation where machine translation handles the initial translation task partially, producing output that requires human refinement. This partial action approach allows machine systems to handle routine translation work while human translators focus only on reviewing and correcting specific areas, reducing overall labor requirements while maintaining high accuracy.
Solution Approach 2:
The patent segments the translation process into distinct stages: machine translation generation, automated quality checking, and human translator review. By dividing the workflow into manageable segments, the system optimizes each stage independently, allowing machine systems to handle high-volume initial translation while human translators concentrate on quality assurance tasks.
3Manufacturing precision
If statistical process analytics are applied to analyze translation memory and machine translation results, then translation quality is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent creates a multi-functional statistical process control system that performs multiple tasks: analyzing translation memory data, evaluating machine translation quality, generating analytics reports, and providing feedback for improvement. This universal system handles diverse analytical functions through integrated algorithms, reducing the need for separate specialized systems and managing complexity through consolidation.
Data Source
AI summary
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.