Semantic Consistency Model for Screenshot Translation Verification
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Solution Overview
Problem
Conventional language translation methods, particularly in-context testing, face challenges in ensuring translation quality and accuracy, especially in identifying mistranslation, partial translation, and inconsistent translations in screenshots, which are time-consuming and costly to manually verify.
Innovation Solution
A computer-implemented method using a trained semantic consistency model that compares linguistic, image, and text location dimensions of two screenshots to determine semantic consistency, enabling efficient and accurate verification of translation quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of translation quality in screenshots is performed, then translation accuracy can be ensured, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical verification with an automated semantic consistency model that uses machine learning to compare screenshots and detect translation inconsistencies. The model processes linguistic content, image characteristics, and text location data automatically, eliminating the need for human reviewers while maintaining high accuracy in detecting translation errors.
2Reliability
If comprehensive translation quality checking is performed manually, then all types of errors can be detected, but the process becomes costly and inefficient
Solution Approach 1:
The system performs self-service quality verification by automatically comparing translated screenshots against reference screenshots using the semantic consistency model. The model independently evaluates linguistic accuracy, visual consistency, and layout preservation without requiring external human intervention, thereby maintaining high reliability while dramatically improving verification efficiency.
3Measurement precision
If multiple dimensions of screenshot comparison are performed, then semantic consistency accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the screenshot comparison task into three distinct analytical dimensions: linguistic content comparison, image characteristic comparison, and text location comparison. Each dimension is processed separately by specialized components within the semantic consistency model, allowing for high accuracy in each aspect while managing overall computational complexity through modular processing.
Data Source
AI summary
A computer-implemented method, according to one embodiment, includes using a trained semantic consistency model to determine a semantic consistency of contents of two screenshots. The trained semantic consistency model bases the semantic consistency on dimensions including: a linguistic comparison of the contents, an image comparison of the contents and a text location comparison of the contents. The method further includes outputting the determined semantic consistency for display on a user device. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method. A system, according to another embodiment, includes a processor, and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to perform the foregoing method.