Linguistic Content Evaluation for Translation Workflow Routing
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Solution Overview
Problem
Current computing technologies lack the ability to measure correlations between linguistic features in unstructured text and user engagement analytic parameters, leading to inefficient translation processes and manual decision-making in translation management platforms, which are laborious and time-consuming.
Innovation Solution
A machine learning model using supervised algorithms correlates linguistic features with user engagement parameters to determine whether unstructured text should be edited or translated, and recommendation engines drive automated workflow decisions based on NLP techniques to optimize translation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual content evaluation processes are used to make workflow routing decisions, then flexibility in decision-making is maintained, but productivity and time efficiency deteriorate due to laborious manual assessments
Solution Approach 1:
The system performs self-service by automatically evaluating content using NLP techniques and machine learning models to determine workflow routing decisions, eliminating the need for manual human assessment while maintaining intelligent, data-driven decision-making capabilities
Solution Approach 2:
The patent replaces the mechanical manual evaluation process with an automated computational system that uses natural language processing, machine learning models, and algorithmic content selection to make workflow routing decisions, thereby increasing productivity without sacrificing decision quality
2Ease of manufacture
If random content selection or oversampling is employed for LQA process, then simplicity of implementation is maintained, but measurement precision deteriorates because it does not identify actual outliers that pose quality risk
Solution Approach 1:
The system performs preliminary action by using NLP techniques and machine learning models to analyze and grade source content before translation, identifying potential quality risks and outliers in advance, which allows targeted LQA sampling based on actual risk assessment rather than random selection
Solution Approach 2:
The patent applies partial action by selectively applying LQA processes only to content that the machine learning model identifies as high-risk or outlier cases, rather than uniformly sampling all content, thereby improving measurement precision while maintaining efficiency
3Speed
If translation is performed without evaluating user engagement analytic parameters, then translation workflow speed is maintained, but loss of information deteriorates because translation may be wasteful when source content has poor user engagement
Solution Approach 1:
The system performs preliminary evaluation of source content using NLP techniques and machine learning models to assess user engagement analytic parameters before translation is initiated, enabling early identification of content that may not benefit from translation and preventing wasteful translation activities
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning model grades source content based on linguistic features and user engagement parameters, and this feedback is used to make informed decisions about whether to proceed with translation, edit source content first, or apply other processing, thereby optimizing resource allocation
Data Source
AI summary
Correlations between a set of linguistic features identified in an unstructured text recited in a source language and a set of user engagement analytic parameters may be measured by a machine learning model selected based on a set of performance metrics from a set of machine learning models trained by a set of supervised machine learning algorithms on (i) a set of unstructured texts recited in the source language and containing the set of linguistic features and (ii) the set of user engagement analytic parameters measured for the set of unstructured texts. The machine learning model grades the unstructured text recited in the source language to determine whether the unstructured text recited in the source language should be (1) edited in the source language and then translated into the target language or (2) translated from the source language to the target language as is.