Active Content-Based Crowdsourcing Task Selection

Crowdsourcing has long established itself as a viable alternative to corpus annotation by domain experts for tasks such as document relevance assessment. The crowdsourcing process traditionally relies on high degrees of label redundancy in order to mitigate the detrimental effects of individually noisy worker submissions. Such redundancy comes at the cost of increased label volume, and, subsequently, monetary requirements. In practice, especially as the size of datasets increases, this is undesirable. In this paper, we focus on an alternate method that exploits document information instead, to infer relevance labels for unjudged documents. We present an active learning scheme for document selection that aims at maximising the overall relevance label prediction accuracy, for a given budget of available relevance judgements by exploiting system-wide estimates of label variance and mutual information.

Our experiments are based on TREC 2011 Crowdsourcing Track data and show that our method is able to achieve state-of-the-art performance while requiring 17% - 25% less budget.