Generative Field Translation for Structured CMS Content
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Solution Overview
Problem
Current methods for creating multilingual content in content management systems are expensive, manually intensive, and error-prone, requiring manual input or third-party translation services.
Innovation Solution
A dynamic content translation service using a generative language model dynamically translates content fields by identifying field types and iteratively processing source code string values, preserving the underlying format and structure, and allowing for user editing before publication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual input or third-party translation services are used to create multilingual content, then translation accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The patent introduces an automated translation service as an intermediary between the source content and the target language content. This service uses machine learning models to translate content fields automatically, eliminating the need for manual translation while maintaining acceptable accuracy through iterative processing and structure preservation.
Solution Approach 2:
The patent replaces the mechanical process of manual translation with an automated computational system. The machine learning-based translation service processes content fields algorithmically, substituting human manual input with automated text processing and translation algorithms.
2Measurement precision
If manual translation methods are used, then translation quality is improved, but labor intensity increases
Solution Approach 1:
The translation service enables self-service translation by automatically processing content fields without requiring human translators. The system extracts text from content fields, translates it using machine learning models, and inserts the translated text back into the corresponding fields, making the translation process autonomous.
Solution Approach 2:
The patent replaces manual translation labor with automated machine learning-based translation algorithms. The system uses computational models to perform translation tasks that would otherwise require human translators, significantly reducing labor intensity while maintaining translation quality.
3Adaptability or versatility
If third-party translation services are integrated, then translation capability is improved, but system complexity increases
Solution Approach 1:
The patent merges the translation capability directly into the content management system by integrating machine learning models within the existing field processing architecture. This consolidation eliminates the need for separate third-party translation service integrations, reducing system complexity while maintaining translation capability.
Solution Approach 2:
The translation service is designed to work with multiple field types (text fields, rich text fields, code blocks) within a single unified system. This multi-functional approach allows the same translation mechanism to handle various content types without requiring separate integration processes for each field type.
4Productivity
If automated translation is implemented, then productivity is improved, but translation accuracy may worsen
Solution Approach 1:
The system applies translation selectively to specific field types rather than attempting to translate all content uniformly. Different processing approaches are used for different field types (e.g., simple text fields versus code blocks), allowing high productivity for suitable fields while maintaining accuracy through specialized handling where needed.
Solution Approach 2:
The translation process includes iterative processing where the system evaluates translation results and can adjust its approach. The machine learning models can be refined based on feedback from translation outcomes, improving accuracy over time while maintaining high productivity through automated processing.
Data Source
AI summary
A method of data processing is described. The method includes receiving an indication of a content entry and a designated language for translation of fields in the content entry. The method further includes determining that a first subset of the fields are associated with a first type and a second subset of the fields are associated with a second type based on a source code structure associated with the content entry. The method further includes obtaining a first set of string values from the first subset of fields and a second set of string values from the second subset of fields based on iterating through the source code structure. The method further includes translating the fields to the designated language based on using a generative language model to process the first set of string values and the second set of string values.