How to Collect High-Quality Data Cost-Effectively
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Effective Quality Assurance for Data Labels through Crowdsourcing and Domain Expert Collaboration
Researchers and scientists have been using crowdsourcing platforms to collect labeled training data in recent years. The process is cost-effective and scalable, but research has shown that the quality of truth inference is unstable due to worker bias, work variance, and task difficulty. In this demonstration, we present a hybrid system, named IDLE (Integrated Data Labeling Engine), that brings ...
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