Improving data and model quality in crowdsourcing using cross-entropy-based noise correction

Crowdsourcing services provide a fast, efficient, and cost-effective approach to obtaining labeled data, particularly for human-like tasks. In a crowdsourcing scenario, after ground truth inference methods have been employed to obtain integrated instance labels, label noise remains present in the integrated labels. Label noise handling techniques can then be implemented to mitigate the effects of this noise. In this study, we propose a Cross-Entropy-based Noise Correction (CENC) method for crowdsourcing. CENC uses the entropies of the label distributions generated from multiple noisy label sets to filter noisy instances. It then exploits the cross-entropies between each possible true class probability distribution and each predicted class probability distribution to rectify the noisy instances. Using both simulated benchmark data and real-world crowdsourced data, we show that CENC outperforms all other existing state-of-the-art noise correction methods. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

Crowdsourcing services offer a fast and cost-effective way to obtain labeled data, but label noise is commonly present. This study introduces a Cross-Entropy-based Noise Correction method that outperforms existing techniques in mitigating label noise in crowdsourced data.