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Achieving Budget-optimality with Adaptive Schemes in Crowdsourcing
[article]
2017
arXiv
pre-print
Crowdsourcing platforms provide marketplaces where task requesters can pay to get labels on their data. Such markets have emerged recently as popular venues for collecting annotations that are crucial in training machine learning models in various applications. However, as jobs are tedious and payments are low, errors are common in such crowdsourced labels. A common strategy to overcome such noise in the answers is to add redundancy by getting multiple answers for each task and aggregating them
arXiv:1602.03481v3
fatcat:qorrngjnffe7fjkd2lnnjbvwfe