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Toward a Robust and Universal Crowd-Labeling Framework

2019-11-25 | |  55 |   40 |   0
Abstract One of the main challenges in crowd-labeling is to control for or determine in advance the proportion of low-quality/malicious labelers. We propose methods that estimate the labeler and data instance related parameters using frequentist and Bayesian approaches. All these approaches are based on expert-labeled instance (ground truth) for a small percentage of data to learn the parameters. We also derive a lower bound on the number of expertlabeled instances needed to get better quality labels.

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