Translation Management System Confidence Routing
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Solution Overview
Problem
Ecommerce transactions face challenges in maintaining accuracy when dealing with different languages, as inaccuracies can lead to lost sales or unhappy customers, highlighting the need for precise translation in publication systems.
Innovation Solution
The implementation of a translation management system (TMS) that combines machine translation (MT) with human translation, using confidence scoring and user feedback to determine the quality of translations, allowing for automatic decision-making on whether to use MT alone, combine MT with human translation, or rely solely on human translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine translation is used alone, then translation speed and cost are improved, but translation accuracy deteriorates
Solution Approach 1:
The patent combines machine translation and human translation into a unified system that automatically routes translation requests based on confidence scoring. High-confidence MT translations are used directly for speed, while low-confidence translations are routed to human translators to ensure accuracy, thereby merging the advantages of both approaches.
Solution Approach 2:
The system implements feedback mechanisms where translation quality is continuously evaluated through confidence scoring and user feedback. This feedback loop allows the system to learn from past translations and improve routing decisions, ensuring that accuracy requirements are met while maintaining efficient use of MT resources.
2Measurement precision
If human translation is used alone, then translation accuracy is improved, but translation cost and time consumption increase
Solution Approach 1:
Instead of applying human translation to all cases, the system applies human translation only partially - specifically to low-confidence MT translations that fall below certain thresholds. This selective approach ensures accuracy is improved where needed while avoiding the excessive cost and time of universal human translation.
Solution Approach 2:
The system dynamically changes the parameter of translation resource allocation based on confidence score thresholds. By adjusting these thresholds, the system can optimize the balance between using MT alone versus combining with human translation, thereby improving efficiency while maintaining required accuracy levels.
3Reliability
If confidence scoring and feedback mechanisms are implemented, then translation quality control is improved, but system complexity increases
Solution Approach 1:
The system implements self-service through automated confidence scoring and threshold-based routing decisions. The translation system automatically evaluates its own output quality and directs requests to appropriate resources without requiring complex external management, thereby improving quality control while limiting complexity growth.
Data Source
AI summary
A system receives original content from a user for translating to translated content. If a machine is to be used for translating, the system determines whether the machine-translated content is to be used as the translated content, or whether the machine-translated content should be transmitted to human translators for scoring or review. If the machine-translated content is not to be used as the translated content, it is sent to human translators for scoring or review. If the machine-translated content is to be used as the translated content, the machine-translated content may still be transmitted to human translators for scoring or review, the results used for machine learning. If a machine is not to be used for translating, the original content is sent to human translators for translating. The foregoing determinations are made based on user information or on statistical analysis.