ML Translation Guidelines for Consistent Entity-Specific Output
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Solution Overview
Problem
Conventional automated language translation techniques fail to account for entity-specific and language-specific translation preferences, relying on manual human analysis which is time-consuming, labor-intensive, and results in inconsistent application of guidelines, limiting scalability and translation quality.
Innovation Solution
A translation system utilizing machine learning models, including a guideline extraction model, translation model, and validation model, automatically extracts and applies entity-specific and language-specific guidelines from translation documents, generating translated text while ensuring adherence to predefined quality thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual human analysis is used to extract and apply translation guidelines, then translation quality can be maintained through human judgment, but the process becomes time-consuming, labor-intensive, and inconsistent
Solution Approach 1:
The patent replaces the mechanical system of manual human analysis with an automated machine learning-based natural language processing system. The guideline extraction model, translation model, and validation model work together to automatically extract guidelines from translation documents, apply them to source text, and validate the translated output, eliminating the need for manual human intervention in the guideline application process while maintaining consistency and quality.
Solution Approach 2:
The translation system performs self-service by automatically extracting its own translation guidelines from provided translation documents, applying these guidelines to perform the translation, and validating its own output against the extracted guidelines. This self-contained automated process eliminates dependency on manual human analysis while maintaining the quality standards embedded in the translation documents.
2Adaptability or versatility
If manual human analysis is used for translation guideline application, then entity-specific preferences can be considered, but scalability is limited due to the labor-intensive nature of the process
Solution Approach 1:
The patent replaces manual human analysis with an automated machine learning system that can process unlimited volumes of translation requests. The system reads entity-specific translation documents, extracts relevant guidelines, and applies them automatically to translate source text while maintaining the entity-specific preferences that would otherwise require manual human judgment for each translation task.
Solution Approach 2:
The translation system is designed to be universal and multi-functional, capable of handling translations for multiple entities with different entity-specific preferences. By automatically extracting guidelines from provided translation documents for each entity, the single system can adapt to serve multiple clients with their unique translation requirements, achieving both adaptability and scalability simultaneously.
3Reliability
If manual human analysis is used to ensure guideline adherence, then translation quality can be maintained, but translation time increases and consistency across different text sizes is reduced
Solution Approach 1:
The validation model operates continuously and automatically to check translated text against extracted guidelines without interruption or manual intervention. This continuous automated validation ensures guideline adherence is maintained throughout the translation process while significantly reducing the time required compared to manual human analysis, as the machine can perform validations instantaneously and consistently across all translation outputs regardless of text size.
Data Source
AI summary
In accordance with the described techniques, a system receives a plurality of facets describing language-agnostic aspects of language translation, a translation document describing language-specific rules for translating from a source language to a target language, and a source text in the source language. Using one or more machine learning models, a plurality of guidelines are extracted from the translation document and assigned to respective facets of the plurality of facets. The system translates the source text to a translated text in the target language using one or more machine learning models conditioned on the plurality of guidelines assigned to the respective facets.