Crowdsourcing Control: Moving Beyond Multiple Choice

TL;DR

This paper introduces LazySusan, a probabilistic graphical model and decision-theoretic controller for crowdsourcing free-response tasks with infinite outcome spaces, improving accuracy and utility over traditional methods.

Contribution

It presents a novel model and controller for crowdsourcing tasks without predefined answer sets, along with an EM algorithm for joint learning and inference.

Findings

LazySusan reduces 83.2% of errors on SAT Math questions.

The EM algorithm outperforms majority-voting on a visualization task.

Live experiments demonstrate improved net utility over state-of-the-art strategies.

Abstract

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.