Translation Bug Prediction Classifier for UI Text
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Solution Overview
Problem
The challenge in translating user interface text strings into multiple languages efficiently is exacerbated by the occurrence of mistranslations, which are difficult to identify due to the logistical issues and linguistic complexities, especially in a global market with tight schedules and fierce competition.
Innovation Solution
A translation bug prediction model is developed using machine learning to identify potential error sources in user interface text strings by analyzing historic translation data and applying a statistical model, such as a naïve Bayesian classifier, to associate an attention flag with resources likely to cause errors, thereby enabling targeted review and testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic translation is used to translate text strings efficiently, then translation productivity is improved, but translation reliability deteriorates due to occasional mistranslations
Solution Approach 1:
The patent applies preliminary action by using a translation bug prediction model to identify potential error sources in translation resources before automatic translation occurs. The system analyzes translation resources using machine learning to predict which ones are likely to cause mistranslations, allowing preventive measures to be taken before the actual translation process, thus maintaining high productivity while improving reliability.
2Reliability
If translation administrator reviews each translation target manually, then translation reliability is improved, but translation productivity deteriorates due to time-consuming manual review
Solution Approach 1:
The patent applies local quality by making different parts of the translation process have different levels of manual review intensity. Instead of uniformly reviewing all translations, the system uses the prediction model to identify high-risk translation resources and directs manual review attention only to those specific cases. This selective approach maintains translation reliability for problematic areas while preserving overall productivity by avoiding unnecessary manual review of low-risk translations.
3Reliability
If comprehensive manual review of all translation resources is performed, then translation reliability is improved, but loss of time increases due to extensive review requirements
Solution Approach 1:
The patent applies partial action by performing manual review only on a subset of translation resources that are predicted to have high error probability. The prediction model identifies these high-risk cases, and manual review is applied partially only to them rather than excessively to all translation resources. This reduces the time loss associated with comprehensive review while maintaining reliability by focusing review efforts where they are most needed.
Data Source
AI summary
In one embodiment, a translation system may use a translation bug prediction model to more efficiently identify translation errors in a user interface text string. The translation system may apply a translation bug prediction model to a translation resource to identify a potential error source. The translation system may associate an attention flag with the translation resource when identified as the potential error source. The translation system may execute an automatic translation of the translation resource to create a translation target.