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The Influence of Input Data Complexity on Crowdsourcing Quality

Crowdsourcing has a huge impact on data gathering for NLP tasks. However, most quality control measures rely on data aggregation methods which are only employed after the crowdsourcing process and thus cannot deal with different worker qualifications during data gathering. This is time-consuming and cost-ineffective because some datapoints might have to be re-labeled or discarded. Training workers and distributing work according to worker qualifications beforehand helps to overcome this limitation. We propose a setup that accounts for input data complexity and allows only a set of workers that successfully completed tasks of rising complexity to continue work on more difficult subsets. Like this, we are able to train workers and at the same time exclude unqualified workers. In initial experiments, our method achieves higher agreement with four annotations by qualified crowd workers compared to five annotations from random crowd workers on the same dataset.