Translation Quality Detection Model for Machine Translation Systems
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Solution Overview
Problem
Current machine translation technologies face challenges in efficiently detecting translation quality, relying on manual editing and simple comparison methods which are inefficient and lack precision.
Innovation Solution
A translation quality detection method and apparatus that utilizes a translation quality detection model trained on source and machine translation data, considering the application scenario, to determine translation quality by processing the source and machine translations, and using a pre-trained model such as a neural network to improve efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual editing and simple comparison methods are used to detect translation quality, then the detection process is simple to implement, but the efficiency and precision of translation quality detection are low
Solution Approach 1:
The patent introduces an automated translation quality detection system as an intermediary between the machine translation process and quality assessment. This system uses trained detection models to automatically evaluate translation quality, replacing manual editing and comparison methods, thereby improving both precision and efficiency simultaneously
Solution Approach 2:
The patent replaces the mechanical manual editing and comparison process with an automated detection system based on trained models. This substitution eliminates the need for human editors to manually compare source and target texts, significantly improving detection efficiency while maintaining or enhancing precision through systematic automated analysis
2Adaptability or versatility
If general translation quality detection methods are used, then the detection process is straightforward, but the detection is not targeted enough for specific application scenarios
Solution Approach 1:
The patent implements dynamic detection models that can adapt to different application scenarios. The system adjusts its detection criteria and parameters based on the specific scenario (e.g., news translation, literary translation, technical documentation), allowing high adaptability without requiring completely separate systems for each scenario
Solution Approach 2:
The patent changes detection parameters based on application scenarios. Different scenarios have different quality requirements, and the system adjusts detection thresholds, weightings, and criteria accordingly. This allows the same detection framework to serve multiple scenarios with varying complexity requirements
Data Source
AI summary
A translation quality detection method includes obtaining a source text and a machine translation of the source text. The machine translation is obtained after a machine translation system translates the source text. The method also includes determining a translation quality of the machine translation based on the source text, the machine translation, and an application scenario of the machine translation.