Quality-Prediction Engine for Machine Translation Trust Levels
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Solution Overview
Problem
Machine-generated translations lack assurance of translational accuracy, relying on human expertise for verification, which is limited and inefficient for cross-language communication.
Innovation Solution
A quality-prediction engine is trained to compare machine-generated translations with human-generated translations, generating a mapping to determine trust levels associated with translational accuracy, allowing for accurate and reliable machine-generated translations without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine translation is used to translate documents, then translation speed and efficiency are improved, but translational accuracy and reliability deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where multiple machine translation engines generate translations, and a quality prediction engine provides feedback on translation quality. This feedback loop allows the system to identify and correct errors, improving reliability while maintaining high translation speed through automated processing.
Solution Approach 2:
The patent introduces a quality prediction engine as an intermediary between the machine translation engines and the final output. This intermediary component evaluates translations from multiple engines and selects or refines the best translation, thereby improving accuracy without requiring human intervention and maintaining productivity.
2Reliability
If human expertise is used to verify translation accuracy, then translational reliability is improved, but time consumption and cost increase
Solution Approach 1:
The patent implements a self-service mechanism where the quality prediction engine automatically evaluates and verifies translation accuracy without human intervention. The system uses trained models to assess translation quality, providing reliable verification while eliminating the time loss and costs associated with human expert review.
Solution Approach 2:
The patent replaces the mechanical system of human expert verification with an automated quality prediction engine. This substitution maintains high translational reliability through accurate quality assessment while dramatically reducing verification time and eliminating the need for human expertise in the verification process.
3Reliability
If multiple machine translation engines are used to improve accuracy, then system complexity increases
Solution Approach 1:
The patent merges multiple machine translation engines and a quality prediction engine into a unified translation system. By combining these components and coordinating their work through a common interface and shared quality metrics, the system achieves high translational accuracy while managing complexity through integrated architecture rather than separate independent systems.
Data Source
AI summary
A quality-prediction engine predicts a trust level associated with translational accuracy of a machine-generated translation. Training a quality-prediction may include translating a document in a source language to a target language by executing a machine-translation engine stored in memory to obtain a machine-generated translation. The training may further include comparing the machine-generated translation with a human-generated translation of the document. The human-generated translation is in the target language. Additionally, the training may include generating a mapping between features of the machine-generated translation and features of the human-generated translation based on the comparison.