Embracing Error to Enable Rapid Crowdsourcing

TL;DR

This paper introduces a rapid crowdsourcing method that accepts errors to significantly increase labeling speed, using response latency and task randomization to maintain accuracy across various tasks.

Contribution

It presents a novel approach that allows for faster crowdsourcing by embracing errors and correcting them through response modeling and task randomization.

Findings

Achieves up to 10x speedup over traditional methods

Effective across diverse labeling tasks

Errors can be rectified without sacrificing accuracy

Abstract

Microtask crowdsourcing has enabled dataset advances in social science and machine learning, but existing crowdsourcing schemes are too expensive to scale up with the expanding volume of data. To scale and widen the applicability of crowdsourcing, we present a technique that produces extremely rapid judgments for binary and categorical labels. Rather than punishing all errors, which causes workers to proceed slowly and deliberately, our technique speeds up workers' judgments to the point where errors are acceptable and even expected. We demonstrate that it is possible to rectify these errors by randomizing task order and modeling response latency. We evaluate our technique on a breadth of common labeling tasks such as image verification, word similarity, sentiment analysis and topic classification. Where prior work typically achieves a 0.25x to 1x speedup over fixed majority vote, our approach often achieves an order of magnitude (10x) speedup.