Pairwise HITS: Quality Estimation from Pairwise Comparisons in Creator-Evaluator Crowdsourcing Process

No. 1: Thirty-First AAAI Conference On Artificial Intelligence

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 31

AAAI Technical Track: Human-Computation and Crowd Sourcing

A common technique for improving the quality of crowdsourcing results is to assign a same task to multiple workers redundantly, and then to aggregate the results to obtain a higher-quality result; however, this technique is not applicable to complex tasks such as article writing since there is no obvious way to aggregate the results. Instead, we can use a two-stage procedure consisting of a creation stage and an evaluation stage, where we first ask workers to create artifacts, and then ask other workers to evaluate the artifacts to estimate their quality. In this study, we propose a novel quality estimation method for the two-stage procedure where pairwise comparison results for pairs of artifacts are collected at the evaluation stage. Our method is based on an extension of Kleinberg's HITS algorithm to pairwise comparison, which takes into account the ability of evaluators as well as the ability of creators. Experiments using actual crowdsourcing tasks show that our methods outperform baseline methods especially when the number of evaluators per artifact is small.

10.1609/aaai.v31i1.10634