Millionaire: A Hint-guided Approach for Crowdsourcing

TL;DR

This paper introduces a hint-guided crowdsourcing approach inspired by the Millionaire game show, enabling workers to use hints to improve label quality while maintaining cost-effectiveness and worker quality detection.

Contribution

It proposes a hybrid-stage setting with a novel payment mechanism that encourages high-quality workers to rely less on hints, enhancing label quality and worker assessment.

Findings

Ensures high-quality labels with low cost

Effectively detects high-quality workers

Encourages workers to use hints judiciously

Abstract

Modern machine learning is migrating to the era of complex models, which requires a plethora of well-annotated data. While crowdsourcing is a promising tool to achieve this goal, existing crowdsourcing approaches barely acquire a sufficient amount of high-quality labels. In this paper, motivated by the "Guess-with-Hints" answer strategy from the Millionaire game show, we introduce the hint-guided approach into crowdsourcing to deal with this challenge. Our approach encourages workers to get help from hints when they are unsure of questions. Specifically, we propose a hybrid-stage setting, consisting of the main stage and the hint stage. When workers face any uncertain question on the main stage, they are allowed to enter the hint stage and look up hints before making any answer. A unique payment mechanism that meets two important design principles for crowdsourcing is developed. Besides, the proposed mechanism further encourages high-quality workers less using hints, which helps identify and assigns larger possible payment to them. Experiments are performed on Amazon Mechanical Turk, which show that our approach ensures a sufficient number of high-quality labels with low expenditure and detects high-quality workers.