  Crowdsourcing offers an affordable and scalable means to collect relevance
judgments for IR test collections. However, crowd assessors may show higher
variance in judgment quality than trusted assessors. In this paper, we
investigate how to effectively utilize both groups of assessors in partnership.
We specifically investigate how agreement in judging is correlated with three
factors: relevance category, document rankings, and topical variance. Based on
this, we then propose two collaborative judging methods in which a portion of
the document-topic pairs are assessed by in-house judges while the rest are
assessed by crowd-workers. Experiments conducted on two TREC collections show
encouraging results when we distribute work intelligently between our two
groups of assessors.
