Container-Based Multilingual Documents for Translation Accuracy
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Solution Overview
Problem
Conventional methods for translating multi-lingual documents are inefficient and prone to errors, as they fail to accurately convey context and intent across languages.
Innovation Solution
A system and method for automated translation and creation of multi-lingual documents that includes generating documents with containers, translating text between languages, and allowing user review and editing through a user interface, with suggested formatting based on document type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct translation is used to translate documents between languages, then translation speed is improved, but translation accuracy and intent preservation deteriorate
Solution Approach 1:
The patent uses an AI model as an intermediary between the source and target languages. Instead of direct translation, the system generates multiple candidate translations and selects the most accurate one, thereby maintaining both speed and accuracy through the mediating AI processing layer
Solution Approach 2:
The system performs preliminary actions by generating multiple candidate translations before finalizing the output. This preliminary generation of options allows for subsequent selection and refinement, ensuring accuracy while maintaining efficiency through automated processing
2Reliability
If manual translation review is performed to improve accuracy, then translation quality is improved, but time consumption and operational complexity worsen
Solution Approach 1:
The system performs self-service by automatically generating, evaluating, and selecting translations without requiring manual human review. The AI model autonomously quality-checks its own output by comparing candidate translations against the source text and selecting the best match, thereby maintaining high quality while eliminating time-consuming manual processes
Solution Approach 2:
The system implements feedback mechanisms where the AI model evaluates its own candidate translations against the source text and selection criteria. This automated feedback loop ensures quality control without external human intervention, reducing time consumption while maintaining translation quality
3Reliability
If context and intent are fully preserved in translation, then translation accuracy is improved, but processing complexity and time worsen
Solution Approach 1:
The system changes parameters by adjusting the AI model's processing depth and candidate generation based on the specific translation task. For routine translations, it uses faster processing with fewer candidates, while for complex texts requiring context preservation, it increases processing depth, thereby balancing accuracy with manageable complexity
Data Source
AI summary
A method comprising using at least one hardware processor to: receive instruction to generate a particular type of document comprising a plurality of containers, and wherein the document has a primary language and a secondary language; generate the document with the plurality of containers; receive text in either the primary language for certain of the plurality of containers; populate the text into the corresponding containers of the plurality of containers; automatically translate the text into the secondary language and populate the translated text into the corresponding containers of the plurality of containers; present the document with the text to a user via a user interface for review and editing; and identifying certain text in the text int eh primary language and suggesting proper formatting for the identified text based on a type of document being created.