Repeated Labeling Using Multiple Noisy Labelers

This paper addresses the repeated acquisition of labels for data itemswhen the labeling is imperfect. We examine the improvement (or lackthereof) in data quality via repeated labeling, and focus especially onthe improvement of training labels for supervised induction. With theoutsourcing of small tasks becoming easier, for example via Amazon'sMechanical Turk, it often is possible to obtain less-than-expertlabeling at low cost. With low-cost labeling, preparing the unlabeledpart of the data can become considerably more expensive than labeling.We present repeated-labeling strategies of increasing complexity, andshow several main results. (i) Repeated-labeling can improve labelquality and model quality, but not always. (ii) When labels are noisy,repeated labeling can be preferable to single labeling even in thetraditional setting where labels are not particularly cheap. (iii) Assoon as the cost of processing the unlabeled data is not free, even thesimple strategy of labeling everything multiple times can giveconsiderable advantage. (iv) Repeatedly labeling a carefully chosen setof points is generally preferable, and we present a set of robusttechniques that combine different notions of uncertainty to select datapoints for which quality should be improved. The bottom line: theresults show clearly that when labeling is not perfect, selectiveacquisition of multiple labels is a strategy that data miners shouldhave in their repertoire. For certain label-quality/cost regimes, thebenefit is substantial