Dynamic Test Question Generation for Annotation Quality Control
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Solution Overview
Problem
Existing annotation methods face challenges with limited annotation throughput and quality in managed crowds, and low quality but high throughput in open crowds, due to difficulties in controlling worker quality and inefficiencies in generating and recycling test questions.
Innovation Solution
A system dynamically generates test questions and performs automatic quality control by using annotation platform servers to create and assign test questions based on submitted results, ensuring consistent and high-quality annotations without manual pre-generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test questions are manually generated in advance for quality control, then worker competency can be confirmed, but the process becomes laborious and the number of available test questions is limited
Solution Approach 1:
The system automatically generates test questions from the annotation data itself, eliminating the need for manual question creation. The annotation data serves its dual purpose as both the work product and the source material for quality control questions, making the system self-sufficient in generating its own test questions.
Solution Approach 2:
The system introduces an automatic question generation mechanism that acts as an intermediary between the annotation data and the quality control process. This intermediary automatically transforms annotation responses into test questions, bridging the gap between data collection and quality verification without manual intervention.
2Productivity
If a large pool of open crowd workers is used, then annotation throughput increases, but annotation quality decreases due to difficulty in controlling worker quality
Solution Approach 1:
The system implements a feedback mechanism where test questions are continuously generated from annotation responses and used to verify worker competency. Workers who fail to answer generated questions correctly are identified and excluded, creating a closed-loop quality control system that adapts to each worker's performance in real-time.
Solution Approach 2:
The test question set is dynamic rather than static - new questions are continuously generated from incoming annotation data, and the system adapts to each worker's responses. This dynamic approach allows the system to maintain high quality standards while scaling to large worker pools, as each worker is evaluated against freshly generated questions rather than a fixed set.
3Quantity of substance
If the same test questions are recycled repeatedly, then the number of available test questions increases, but the quality control effectiveness decreases
Solution Approach 1:
The system ensures continuous generation of fresh test questions from the ongoing annotation process. Rather than recycling a finite set of questions, the system continuously transforms new annotation responses into new test questions, ensuring an endless supply of unique questions that maintain their effectiveness for quality control.
Solution Approach 2:
The system changes the parameters of test questions dynamically by generating them from varying annotation data. Each annotation response becomes the basis for potentially different test questions, ensuring that questions vary in content, difficulty, and focus while maintaining the same quality control function.
Data Source
AI summary
Dynamically generated test questions for automatic quality control of submitted annotations is disclosed, including: distributing a first subset of queries from input data associated with an annotation job to a plurality of annotator devices via an annotation platform; receiving a set of annotation results corresponding to the first subset of queries from the annotator devices; dynamically generating a set of test questions and corresponding test answers based on the first subset of queries and the set of annotations results; distributing a second subset of queries from the input data and the set of test questions to the annotator devices via the annotation platform; and performing an action corresponding to a contributor with respect to the annotation job based on a submitted answer, received from a corresponding annotator device, corresponding to at least one test question of the set of test questions.