Crowdsourced Translation Priority Queuing System
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Solution Overview
Problem
Crowdsourcing for dynamic and ephemeral content translation faces challenges due to high latency and limited resources, as it requires specialized skills and cannot efficiently prioritize content for timely and accurate translation.
Innovation Solution
A method and system that evaluates and prioritizes content for crowdsourcing based on metrics such as criticality, frequency of translation requests, and user expertise, using a queuing system to ensure that high-priority segments are translated first, incorporating Machine Translation scores and user preferences to optimize translation quality and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If crowdsourcing is used for translation, then translation cost is reduced, but latency increases due to the unsupervised nature requiring additional review steps
Solution Approach 1:
The system performs preliminary actions by pre-evaluating content metrics (criticality, freshness, complexity) and pre-queuing content before translation requests arrive. This allows the system to be prepared and reduce actual translation latency while maintaining cost-effective crowdsourcing.
Solution Approach 2:
The system implements feedback mechanisms where translation results are evaluated and used to adjust priority assignments and queue management. This continuous feedback loop optimizes the balance between cost and latency by learning from actual translation outcomes and resource availability.
2Reliability
If all content is translated through crowdsourcing, then translation quality improves, but resource limitations prevent timely translation of dynamic content
Solution Approach 1:
The system applies local quality by assigning different priority levels and quality requirements to different content segments based on their specific characteristics (criticality, freshness, complexity). High-criticality content receives premium treatment with higher quality standards, while less critical content uses standard crowdsourcing processes.
Solution Approach 2:
The system performs partial action by selectively applying crowdsourcing only to content that requires it based on priority evaluation. Machine translation is used for low-priority content, while crowdsourcing is reserved for high-priority content, optimizing resource utilization and translation speed.
3Loss of time
If priority-based queuing is implemented, then critical content is translated first, but system complexity increases due to metric evaluation and queue management
Solution Approach 1:
The system implements self-service by automatically evaluating content metrics and assigning priorities without manual intervention. The priority queuing system autonomously manages content allocation based on predefined criteria, reducing operational complexity while improving translation timing for critical content.
Data Source
AI summary
A method of translating information using crowdsourcing includes evaluating a metric related to a content to be translated, determining a priority for the content, queuing the content for the crowdsourcing based on the priority determined from the metric, and translating the information from a language to another language using the crowdsourcing.