An error consistency based approach to answer aggregation in open-ended crowdsourcing

Crowdsourcing plays a vital role in today's AI industry. However, existing crowdsourcing research mainly focuses on those simple tasks that are often formulated as label classification, while complex open-ended tasks such as question answering and translation have not received much attention. Such tasks usually have open solution spaces and non-unique true answers, which pose great challenges for designing effective crowdsourcing algorithms. In this work, we are concerned specifically with complex text annotation crowdsourcing tasks, where each answer of a task is in the form of free text. We propose an error consistency-based approach to inferring a satisfying result from a set of open-ended answers. First, each answer is represented with two vectors that capture the local word collocation and the global sentence semantics respectively. Second, the true answer is approximated by the sum of the answer vectors weighted by the reciprocals of their respective errors. Third, an algorithm called AEC (Aggregation based on Error Consistency) is designed to infer the aggregated result by maximizing the consistency of the errors of an answer in two vector spaces. Experimental results on two datasets demonstrate the effectiveness of our approach.