Localization Platform Using Previously Translated Content Matching
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Solution Overview
Problem
Manual translation of textual content is time-consuming, error-prone, and difficult to scale efficiently, especially for businesses with large volumes of content that need to be translated quickly and accurately for international markets.
Innovation Solution
A localization platform that leverages previously translated content by searching a database for matching text elements and using them to automate the translation process, reducing the need for manual translation by providing linguists with matching text elements and metadata for quality assurance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual translation is used to ensure high accuracy and quality, then translation quality is improved, but translation speed and productivity deteriorate
Solution Approach 1:
The translation process is segmented into multiple stages: automated machine translation for initial draft, extraction of source text elements, matching with previously translated content, and selective manual review. This segmentation allows different quality requirements to be applied to different parts of the translation workflow, maintaining high quality for critical content while improving overall speed.
Solution Approach 2:
The system performs preliminary actions by pre-translating content elements and storing them in a database for future reuse. When new translation requests arrive, the system first checks for matching previously translated content before initiating new translation work, thereby reducing redundant manual translation efforts and improving productivity.
2Reliability
If manual translation is used to handle large volumes of content, then translation accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system creates copies of previously translated content elements and stores them in a database. When new translation requests are received, the system searches for and reuses matching copied translations rather than performing new manual translation, thereby reducing time consumption and cost while maintaining consistency and accuracy across translations.
Solution Approach 2:
The translation system serves itself by automatically checking new content against its own database of previously translated elements. This self-service mechanism identifies reusable translations without human intervention, reducing the time and cost burden of manual translation for large volumes of content.
3Manufacturing precision
If manual translation is used to accommodate linguistic differences and idioms, then translation quality is improved, but scalability deteriorates
Solution Approach 1:
The translation workflow is segmented to handle different content types differently. Common, standardized content elements are processed through automated matching with previously translated content, while unique, complex content requiring linguistic nuance is directed to manual translation. This segmentation enables the system to scale efficiently while maintaining quality for content requiring linguistic expertise.
4Productivity
If automated machine translation is used to improve speed and reduce cost, then productivity is improved, but translation accuracy and quality deteriorate
Solution Approach 1:
The system introduces an intermediary layer between automated machine translation and final output. This intermediary performs fuzzy matching of source text elements against previously translated content, identifying candidates for reuse that maintain linguistic accuracy and cultural appropriateness, thereby bridging the gap between automated speed and manual quality.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for translating textual content. The technique includes receiving a request to translate an element of source text from an origin language to a target language and searching a database for an element of matching text in the origin language that at least partially matches the element of source text. The technique further includes, if an element of matching text is found in the database, then reading from the database an element of previously translated text that is mapped to the element of matching text and includes at least one word that is translated into the target language, and transmitting the element of source text, the element of matching text, and the element of previously translated text to a location for translation, or if an element of matching text is not found in the database, then transmitting the element of source text to the location for translation.