Quality Control for Machine Language Interpreters
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Solution Overview
Problem
Conventional human-based over-the-phone interpretation systems face limitations due to unavailability of interpreters and lack of contextual information retention, leading to reduced efficacy and scalability.
Innovation Solution
A machine language interpretation system with quality control compliance, utilizing a processor and AI to route requests to machine language interpreters, monitor quality criteria in real-time, and store transcripts for machine learning improvements, ensuring consistent and scalable language interpretation services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human language interpreters are used for over-the-phone interpretation sessions, then users can obtain human language interpretation services, but the system has limited availability and retention of contextual information
Solution Approach 1:
The system segments the interpretation service into multiple machine language interpreters (MLIs) that can be independently activated. Each MLI is assigned to specific language pairs and can be routed to handle specific interpretation requests, ensuring continuous availability while maintaining service quality through distributed architecture
Solution Approach 2:
The system implements feedback mechanisms where interpretation requests and responses are monitored and evaluated. This allows the system to learn from previous interactions, retain contextual information across sessions, and continuously improve interpretation quality while maintaining consistent service availability
2Productivity
If multiple machine language interpreters are deployed to improve availability, then service coverage increases, but monitoring quality compliance becomes more complex
Solution Approach 1:
The quality control system is designed as a universal platform that can monitor and evaluate multiple MLIs simultaneously. It implements standardized quality criteria that apply across all interpreters, allowing the system to manage increased interpreter capacity without proportionally increasing control complexity
Solution Approach 2:
The system changes the parameters of quality monitoring by implementing automated evaluation metrics and algorithms. Instead of manual review of each interpretation, the system uses configurable quality parameters and thresholds that can be adjusted based on service requirements, simplifying the monitoring of multiple interpreters
Data Source
AI summary
A configuration provides quality control compliance for a plurality of machine language interpreters. A processor receives a plurality of requests for human-spoken language interpretation from a first human-spoken language to a second human-spoken language. Further, processor routes the plurality of requests to a plurality of machine language interpreters. In addition, an artificial intelligence system associated the plurality of machine language interpreters determines one or more quality control criteria. The processor also monitors compliance of the one or more quality control criteria by the plurality of machine language interpreters during simultaneously occurring machine language interpretations performed by the machine language interpreters.