Automated Machine Translation Protocol Selection via Quality Prediction
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Solution Overview
Problem
Machine translations often produce unreliable results due to limitations in data recognition and translation quality, particularly for less frequent language combinations and complex content types, leading to errors and the need for manual intervention.
Innovation Solution
An automated system using machine learning models predicts translation quality scores to select appropriate translation protocols, balancing machine-based and human-based translations, and builds a translation memory to store accurate translations, allowing for hybrid models that incorporate human review when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine translation is used to process translation requests, then productivity is improved, but translation reliability deteriorates
Solution Approach 1:
The system dynamically selects translation protocols based on content attributes and predicted translation quality scores. Instead of using a fixed machine translation approach, the system adapts the translation method (fully automated, hybrid with human review, or manual) according to the specific characteristics of each translation request, thereby maintaining high productivity while ensuring reliability for complex or low-confidence translations
Solution Approach 2:
The translation quality prediction model acts as an intermediary between the machine translation system and human reviewers. It predicts translation quality scores and uses these predictions to determine when human intervention is necessary, creating a smart filtering mechanism that routes only problematic translations to human reviewers while allowing high-confidence translations to proceed automatically
2Reliability
If human operators review all machine translations, then translation reliability is improved, but productivity deteriorates
Solution Approach 1:
Instead of applying human review to all translations (excessive action), the system applies human review only partially - specifically to translations with low predicted quality scores or complex content attributes. This selective approach ensures that human expertise is concentrated where most needed, maintaining high reliability without the productivity penalty of universal human review
3Reliability
If machine learning models predict translation quality, then translation reliability is improved, but device complexity increases
Solution Approach 1:
The system segments the translation workflow into distinct components: content analysis module, translation quality prediction module, protocol selection module, and execution module. Each module has a specific function and processes specific aspects of the translation request. This segmentation makes the complex system more manageable, modular, and easier to maintain while preserving the reliability benefits of machine learning-based quality prediction
Data Source
AI summary
Systems, devices, and methods are provided for using an automated assessment and evaluation of machine translations. A system may receive a request associated with translating first content from a first language to a second language. The system may translate the first content from the first language to the second language. The system may determine an attribute associated with the first content. The system may determine a translation score associated with second content translated from the first language to the second language and associated with the attribute, the translation score indicative of a machine translation accuracy. The system may determine, and based on the translation score, a translation protocol, and may execute the translation protocol.