Efficient Online Crowdsourcing with Complex Annotations

TL;DR

This paper introduces an online crowdsourcing method for complex annotations, leveraging labeler accuracy inference to optimize the cost-quality trade-off in real-time settings.

Contribution

It proposes a novel online approach for complex annotations that infers labeler accuracy and improves cost efficiency, validated on real-world data.

Findings

Effective in reducing annotation costs

Accurately infers labeler accuracy in real-time

Improves quality of aggregated annotations

Abstract

Crowdsourcing platforms use various truth discovery algorithms to aggregate annotations from multiple labelers. In an online setting, however, the main challenge is to decide whether to ask for more annotations for each item to efficiently trade off cost (i.e., the number of annotations) for quality of the aggregated annotations. In this paper, we propose a novel approach for general complex annotation (such as bounding boxes and taxonomy paths), that works in an online crowdsourcing setting. We prove that the expected average similarity of a labeler is linear in their accuracy \emph{conditional on the reported label}. This enables us to infer reported label accuracy in a broad range of scenarios. We conduct extensive evaluations on real-world crowdsourcing data from Meta and show the effectiveness of our proposed online algorithms in improving the cost-quality trade-off.