Translated Content Performance Scoring via NLP Profiles
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Solution Overview
Problem
Current methods for evaluating translated content lack objective measures to determine the quality and effectiveness of machine, human, or combined translations, leading to potential errors and poor customer satisfaction, without considering how the content is consumed or its return on investment.
Innovation Solution
A computer-implementable method and system using AI/ML-derived translatability and NLP profiles to assess the quality of original content, predict machine translation readiness, and determine content performance scores based on user experience and quality of translated content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine translation is used to translate original documents, then translation efficiency and cost are improved, but translation quality and accuracy deteriorate
Solution Approach 1:
The patent introduces an automated quality evaluation system as an intermediary between machine translation and final content delivery. This system uses NLP analysis, translatability profiling, and multiple quality metrics to objectively assess translated content, enabling identification and correction of machine translation errors without requiring full manual review of all content.
Solution Approach 2:
The patent implements feedback mechanisms where translation quality is continuously measured using automated evaluation metrics including NLP profiles, translatability scores, and content quality assessments. This feedback loop enables iterative improvement of translation processes and allows for selective human review based on identified quality issues.
2Manufacturing precision
If human translation is used to ensure high translation quality, then translation accuracy is improved, but translation cost and time increase
Solution Approach 1:
The patent applies partial human review based on automated quality assessment. Instead of requiring full human translation or review of all content, the system identifies specific segments that require human attention based on quality metrics, applying human expertise only where needed rather than universally.
Solution Approach 2:
The patent changes the parameter of quality assessment from subjective linguistic evaluation to objective automated measurement using NLP profiles, translatability scores, and content quality metrics. This enables data-driven decisions about when human review is necessary, optimizing the balance between quality and resource consumption.
3Measurement precision
If traditional linguistic quality evaluation is used for translated content, then language accuracy is assessed, but user experience and effectiveness are not measured
Solution Approach 1:
The patent creates a multi-functional evaluation system that simultaneously assesses multiple dimensions of translated content: traditional linguistic quality, NLP profile compatibility, translatability metrics, and user experience indicators. This universal evaluation framework handles diverse assessment needs within a single integrated system.
Solution Approach 2:
The patent adds new dimensions to quality evaluation beyond traditional linguistic metrics. It incorporates NLP analysis dimensions, translatability profiling dimensions, and user experience dimensions, transforming quality assessment from a single-dimensional linguistic check to a multi-dimensional evaluation framework.
4Device complexity
If no automated quality evaluation system is implemented, then manual review processes are simpler, but translation errors and poor customer satisfaction increase
Solution Approach 1:
The patent implements self-service quality evaluation where the translation system automatically assesses its own output using NLP analysis, translatability profiling, and content quality metrics. This automated self-evaluation identifies issues without requiring external manual inspection, maintaining process efficiency while improving reliability.
Data Source
AI summary
Described herein are methods and a system for determining performance and providing a content score of translated content from an original source document or content. Quality and fitness of the original source content is checked, and translatability profile and natural language processing (NLP) profile is provided. The translatability profile and natural language processing (NLP) profile are used to provide a level of machine translation level to be performed on the original source document. The original source document or content is translated into a different language. The translated content is checked for quality, which is used along with user experience (UX) of the translated content to provide a content score of the translated content.