Semantic Tree Translation System for Near Real-Time Accuracy
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Solution Overview
Problem
Current machine-based translation methods lack accuracy, particularly in critical domains like healthcare, where inaccurate translations can have severe consequences, and human translators or interpreters are either too slow or costly for real-time needs.
Innovation Solution
A computer-implemented method that translates input text by identifying and replacing portions with semantic structures, organizing them into a semantic tree, matching with a phrase bank, and providing verified translations in the target language, allowing users to confirm the intended meaning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If statistical machine translation programs are used for quick translation output, then translation speed is improved, but translation accuracy and reliability deteriorate
Solution Approach 1:
The patent segments the translation process into distinct phases: (1) semantic structure identification and replacement, (2) semantic tree organization, (3) phrase bank matching, and (4) user verification. This segmentation allows the system to maintain speed through automated processing while ensuring accuracy through structured validation at each stage, directly resolving the contradiction between fast translation and reliable output.
Solution Approach 2:
The patent introduces semantic structures and a phrase bank as intermediary elements between the source text and target translation. These intermediaries serve as a controlled vocabulary framework that guides translation through predefined semantic categories, ensuring consistency and accuracy while maintaining translation speed through efficient matching algorithms.
2Reliability
If human translators or interpreters are employed for accurate translation, then translation accuracy is improved, but translation speed and cost-effectiveness deteriorate
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically performs semantic structure identification, tree organization, and phrase bank matching. This automation eliminates the need for human translators while maintaining high accuracy through structured processing, and significantly improves speed by removing human bottlenecks, thus resolving the contradiction between accuracy and productivity.
Solution Approach 2:
The patent changes the operational parameters of translation from human-dependent processes to automated computational processes. By transforming the translation mechanism into an algorithmic system with defined semantic structures and matching rules, the system achieves both high accuracy (through structured validation) and high productivity (through automated processing), resolving the contradiction between these two parameters.
3Measurement precision
If back-translation is performed to verify translation accuracy, then translation verification is improved, but the verification process fails to detect mistranslations
Solution Approach 1:
Instead of using back-translation (translating from target language back to source language), the patent inverts the verification approach by translating the source text through a controlled semantic framework into the target language, then comparing the result against the original translation. This inversion allows for reliable mistranslation detection by using the semantic structure as a reference standard rather than relying on statistical back-translation.
4Reliability
If multiple professional linguists perform forward translation, editing, back translation, and reconciliation, then translation accuracy is improved, but the process cannot be performed in near real-time
Solution Approach 1:
The patent replaces the mechanical system of multiple human linguists performing sequential tasks (forward translation, editing, back translation, reconciliation) with an automated computational system. The semantic structure identification, tree organization, and phrase bank matching are performed automatically by algorithms, eliminating the time-consuming human review process while maintaining accuracy through structured validation, thus resolving the contradiction between reliability and time loss.
Data Source
AI summary
A computer-implemented method is provided for translating input text from a source language to a target language including receiving, by an interface, the input text in a source language, and identifying, by a processor coupled to the interface, at least one portion of the input text. The method includes replacing each portion with a corresponding sematic structure to produce at least one semantic structure, and organizing the at least one semantic structure into a semantic tree. The method includes matching a portion of the semantic tree to at least one phrase from a stored phrase bank, and providing one or more versions of the at least one phrase in the source language. The method includes receiving a selected version of the set of versions, translating the selected version from the source language to the target language, and providing the selected version in the target language.