  To ensure quality results from crowdsourced tasks, requesters often aggregate
worker responses and use one of a plethora of strategies to infer the correct
answer from the set of noisy responses. However, all current models assume
prior knowledge of all possible outcomes of the task. While not an unreasonable
assumption for tasks that can be posited as multiple-choice questions (e.g.
n-ary classification), we observe that many tasks do not naturally fit this
paradigm, but instead demand a free-response formulation where the outcome
space is of infinite size (e.g. audio transcription). We model such tasks with
a novel probabilistic graphical model, and design and implement LazySusan, a
decision-theoretic controller that dynamically requests responses as necessary
in order to infer answers to these tasks. We also design an EM algorithm to
jointly learn the parameters of our model while inferring the correct answers
to multiple tasks at a time. Live experiments on Amazon Mechanical Turk
demonstrate the superiority of LazySusan at solving SAT Math questions,
eliminating 83.2% of the error and achieving greater net utility compared to
the state-ofthe-art strategy, majority-voting. We also show in live experiments
that our EM algorithm outperforms majority-voting on a visualization task that
we design.
