Dynamic Data Labeling System Balancing Quality and Efficiency
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Solution Overview
Problem
Current data labeling methods face challenges in balancing data labeling quality, efficiency, and labor costs, with manual labeling being costly and time-consuming while automatic labeling often compromises on quality, and there is a need for a method to select the appropriate labeling approach based on specific task requirements and constraints.
Innovation Solution
A method that receives a data labeling request, determines a target labeling task, divides the data into automatic and manual labeling components based on constraints, and selects a labeling procedure based on task type, quality, and metrics to optimize labeling efficiency and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used, then labeling quality is improved, but labeling cost and time consumption increase
Solution Approach 1:
The patent segments the labeling process into multiple stages: automatic pre-labeling stage and manual refinement stage. The labeling system divides labeling tasks into different categories based on difficulty and importance, applying automatic labeling to simple tasks and manual labeling to complex tasks, thereby optimizing the balance between quality and efficiency
Solution Approach 2:
The system performs preliminary automatic labeling before manual review. By pre-labeling data with automated algorithms and then having human labelers only review and correct the results, the system reduces the overall time consumption while maintaining high labeling quality
2Productivity
If automatic labeling is used, then labeling efficiency is improved, but labeling quality deteriorates
Solution Approach 1:
The system implements a feedback mechanism where automatically labeled data is reviewed and corrected by human labelers, and the corrected results are used to continuously improve the automatic labeling algorithms. This closed-loop feedback system enables the automatic labeling quality to progressively improve while maintaining high efficiency
Solution Approach 2:
The system dynamically adjusts labeling parameters such as confidence thresholds and quality requirements based on the specific task characteristics. For high-stakes tasks, the system increases quality requirements and reduces automation extent, while for low-stakes tasks, it increases automation extent to maximize efficiency
3Adaptability or versatility
If multiple labeling methods are used to address different requirements, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent designs a universal labeling system that can handle multiple labeling methods (automatic, manual, semi-automatic) through a unified framework. The system uses a standardized interface and workflow that adapts to different task requirements, eliminating the need for separate systems for different labeling approaches and thereby reducing overall complexity
Data Source
AI summary
The present disclosure provides a method, an apparatus, an electronic device and a storage medium of data labeling. In some embodiments, the method comprises: receiving a data labeling request, the data labeling request including one or more labeling tasks, one or more task types, labeling data, a constraint, one or more labeling index values; determining a target labeling task based on the one or more labeling index values and the one or more labeling tasks; determining a labeling procedure based on the data labeling request and at least one selected from a group consisting of the task type, a labeling quality, and a labeling metric; and labeling the labeling data using the labeling procedure. In certain embodiments, multiple target values, including the data labeling quality, the labeling efficiency and the labeling costs, can be dynamically balanced to achieve better resource allocation and provide an individualized labeling configuration for labeling tasks.