Context-Aware Web UI Translation via Accessibility Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing translation methods for user interface strings in web applications often result in incorrect or incomplete translations due to the lack of context information, leading to poor user experience and the need for time-intensive verification processes.
Innovation Solution
A method that identifies accessibility information associated with original program integrated information (PII) strings, creates translation context datasets, generates translation pairs, and stores them in a two-stage index for accurate and context-based translations, leveraging technologies like WAI-ARIA roles and natural language processing to provide rich context for translators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional translation methods are used for UI strings, then translation process is simple, but translation accuracy deteriorates due to lack of context information
Solution Approach 1:
The patent extracts and stores context information (accessibility information, surrounding elements, DOM structure) along with the UI string before translation. This preliminary action preserves the context that would otherwise be lost, enabling accurate translation by making context information available in advance for the translation process.
Solution Approach 2:
The patent introduces a translation context dataset as an intermediary between the UI string and the translation process. This dataset includes accessibility information, surrounding elements, and DOM structure, serving as a mediator that conveys contextual meaning to the translation system, thereby improving translation accuracy without losing context information.
2Measurement precision
If context information is added to translation process, then translation accuracy improves, but translation process complexity increases
Solution Approach 1:
The patent segments the translation process into distinct components: extracting UI strings, collecting context information (accessibility information, surrounding elements, DOM structure), creating translation context datasets, and performing translation. This segmentation allows each component to be handled separately and systematically, managing complexity while improving accuracy.
Solution Approach 2:
The patent changes the parameters of the translation process by incorporating additional data dimensions (accessibility information, surrounding elements, DOM structure) into the translation context dataset. This parameter enrichment improves translation accuracy while the structured approach to collecting and organizing these parameters manages the increased complexity.
3Measurement precision
If manual verification of translations is performed, then translation quality improves, but time consumption increases
Solution Approach 1:
The patent replaces manual verification with an automated translation system that uses context information (accessibility information, surrounding elements, DOM structure) to perform translations. This substitution eliminates time-consuming manual verification while maintaining high translation quality through context-aware machine translation.
Solution Approach 2:
The translation system performs self-verification by using the collected context information to ensure translation accuracy. The system automatically matches UI strings with their context, performs translation, and validates results without requiring external manual verification, thereby reducing time consumption while maintaining quality.
Data Source
AI summary
Translating applications to a target language includes extracting program integrated information (PII) to be translated and creating translation context datasets based on interpretation of accessibility information associated with particular strings of PII. Translation pairs include PII and corresponding context datasets for context-based translation of application components. A two-stage index contains PII strings for first stage lookup and context datasets for distinguishing duplicate PII strings as a second stage lookup. Real-time translation is facilitated by the two-stage index, which is established by translation pairs and resulting translations.