Dynamic Task Allocation for Crowdsourcing Settings
Abstract
We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers. Such an optimized worker assignment method allows us to boost the efficacy of any popular crowdsourcing estimation algorithm. We consider a mutual information interpretation of the crowdsourcing problem, which leads to a stochastic subset selection problem with a submodular objective function. We present experimental simulation results which demonstrate the effectiveness of our dynamic task allocation method for achieving higher accuracy, possibly requiring fewer labels, as well as improving upon a previous method which is sensitive to the proportion of users to questions.
Cite
@article{arxiv.1701.08795,
title = {Dynamic Task Allocation for Crowdsourcing Settings},
author = {Angela Zhou and Irineo Cabreros and Karan Singh},
journal= {arXiv preprint arXiv:1701.08795},
year = {2017}
}
Comments
Presented at the Data Efficient Machine Learning Workshop at International Conference on Machine Learning (ICML) 2016