Translation Platform Segmentation for Quality and Speed
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Solution Overview
Problem
Current translation platforms face challenges in efficiently managing and automating complex translation processes, including resource allocation, quality assurance, and cost optimization, particularly in handling diverse translation tasks and maintaining high-quality outputs across multiple languages and formats.
Innovation Solution
A platform architecture that combines machine translation with human crowd-sourced translation, utilizing modular modules for task management, resource allocation, and quality control, which breaks down translation tasks into cognizable units (CTUs) and assigns them based on difficulty, outcome objectives, and resource availability, ensuring efficient processing and quality maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine translation is used to increase productivity, then translation speed improves, but translation quality deteriorates
Solution Approach 1:
The translation process is segmented into multiple stages: machine translation for initial draft, followed by human review, editing, and quality assurance. This segmentation allows each stage to focus on specific aspects, with machine handling volume and humans handling quality control.
Solution Approach 2:
Different quality levels are applied to different translation units based on their characteristics. High-priority or complex segments receive more thorough human review, while routine segments may rely more on machine translation with automated quality checks.
2Manufacturing precision
If human crowd-sourced translation is used to maintain quality, then translation quality improves, but processing time increases
Solution Approach 1:
Machine translation performs preliminary work to create a draft version, eliminating the need for humans to translate from scratch. This preliminary action reduces the time humans need to spend while maintaining quality through subsequent review stages.
Solution Approach 2:
Human reviewers perform partial translation work by focusing only on reviewing and editing machine-generated content rather than complete translation. This partial action approach maintains quality while significantly reducing processing time compared to full manual translation.
3Adaptability or versatility
If translation tasks are broken down into cognizable units for better resource allocation, then resource utilization improves, but system complexity increases
Solution Approach 1:
The platform uses universal modules for task management, resource allocation, and quality control that can handle various translation scenarios. These multi-functional modules reduce overall system complexity despite the granular breakdown of translation units.
Solution Approach 2:
The system automatically performs resource allocation, task distribution, and quality assessment without requiring complex manual coordination. Automated self-service mechanisms handle the complexity of managing numerous translation units, allowing efficient resource allocation without proportional increases in system complexity.
Data Source
AI summary
A platform and related components are provided for managing and executing various processes involving distributed, crowd and automated resources, including human and machine language-based translation, are described, including methods and systems for creating and intelligently distributing cognizable translation units among internal workers, outsourcing centers, and crowd workers and methods and systems for on-demand translation.