Model Localization via Dynamic Translation Files
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Solution Overview
Problem
Current localization methods for databases and data models are inefficient, requiring manual intervention and resulting in database size increases and inflexible translation management, as they lack mechanisms for dynamic updates and context-dependent translations.
Innovation Solution
A method and system for model localization that involves parsing a data model to identify translatable terms, generating a seed file with tags, submitting terms to machine translation, and dynamically updating translations within a data analytics application, allowing authorized users to review and modify translations, thus avoiding database modification and enabling flexible translation management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual translation methods are used for database localization, then translation accuracy can be maintained, but productivity is reduced and the process becomes time-consuming
Solution Approach 1:
The patent introduces machine translation as an intermediary tool that handles the initial translation of database objects, while human translators review and refine the results. This intermediary approach allows the system to leverage automated translation speed while maintaining human-level quality control, resolving the contradiction between productivity and translation accuracy.
Solution Approach 2:
The system implements a feedback mechanism where translated terms are submitted for review, and corrections are fed back into the translation process. This iterative feedback loop enables continuous improvement of translation quality while maintaining high productivity through automated initial translation, addressing both accuracy and speed requirements.
2Adaptability or versatility
If database structures are modified to store multiple language versions, then localization capability is achieved, but device complexity and database size increase
Solution Approach 1:
The patent extracts translation management from the database structure itself by storing translations in separate translation files associated with the database model. This separation allows the database to maintain its original simple structure while still achieving full localization capability through external translation resources, resolving the contradiction between adaptability and device complexity.
Solution Approach 2:
Translation files serve as an intermediary layer between the database model and the user interface. Instead of modifying the database structure to accommodate multiple languages, the system uses these intermediary translation files to provide localized presentations, maintaining database simplicity while achieving localization versatility.
3Adaptability or versatility
If translated terms are hard-coded into the database model, then localization is achieved, but adaptability is reduced as updates become difficult
Solution Approach 1:
The patent implements a dynamic translation management system where translation files can be independently updated without modifying the database model structure. This dynamic approach allows translations to be adapted and updated flexibly in response to changing requirements, while the system automatically incorporates these updates, resolving the contradiction between adaptability and ease of maintenance.
Solution Approach 2:
The system segments translation management from the database model by using separate, independently updatable translation files. This segmentation allows each translation file to be modified, updated, or replaced without affecting the underlying database structure, providing both adaptability for updates and ease of maintenance through isolated translation management.
Data Source
AI summary
Implementations of the disclosure provide a method, system and computer program product for model localization. In an implementation of the disclosure, a method for model localization includes parsing a model to identify translatable terms, generating a seed file associating each of the translatable terms with a corresponding tag and replacing each translatable term in the model with a corresponding tag and submitting each of the translatable terms to machine translation for a target language to produce a different translation file mapping each tag from the seed file with a translated term in the target language of a corresponding one of the translatable terms. Then, the model may be deployed in a data analytics application using the different translation file to dynamically translate each translatable term into a corresponding translated term within a user interface to the data analytics application.