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Crowd-labeling emerged from the need to label large-scale and complex data, atedious, expensive, and time-consuming task. One of the main challenges in thecrowd-labeling task is to control for or determine in advance the proportion oflow-quality/malicious labelers. If that proportion grows too high, there isoften a phase transition leading to a steep, non-linear drop in labelingaccuracy as noted by Karger et al. [2014]. To address these challenges, wepropose a new framework called Expert Label Injected Crowd Estimation (ELICE)and extend it to different versions and variants that delay phase transitionleading to a better labeling accuracy. ELICE automatically combines and boostsbulk crowd labels supported by labels from experts for limited number ofinstances from the dataset. The expert-labels help to estimate the individualability of crowd labelers and difficulty of each instance, both of which areused to aggregate the labels. Empirical evaluation shows the superiority ofELICE as compared to other state-of-the-art methods. We also derive a lowerbound on the number of expert-labeled instances needed to estimate the crowdability and dataset difficulty as well as to get better quality labels.

Toward a Robust Crowd-labeling Framework using Expert Evaluation and Pairwise Comparison