Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons
This addresses cost-effective data labeling for machine learning in scenarios where traditional crowdsourcing is challenging due to continuous or complex labels, though it is incremental as it builds on existing Ballpark Learning for classification.
The paper tackles the difficulty of collecting continuous labels from non-expert crowdworkers by extending Ballpark Learning to regression, using group comparisons to infer individual labels with a convex optimization method that is robust to outliers. It demonstrates on real-world datasets that this approach can rival supervised models with many true labels and achieve better results at lower cost than standard labeling.
Crowdsourcing has become a popular method for collecting labeled training data. However, in many practical scenarios traditional labeling can be difficult for crowdworkers (for example, if the data is high-dimensional or unintuitive, or the labels are continuous). In this work, we develop a novel model for crowdsourcing that can complement standard practices by exploiting people's intuitions about groups and relations between them. We employ a recent machine learning setting, called Ballpark Learning, that can estimate individual labels given only coarse, aggregated signal over groups of data points. To address the important case of continuous labels, we extend the Ballpark setting (which focused on classification) to regression problems. We formulate the problem as a convex optimization problem and propose fast, simple methods with an innate robustness to outliers. We evaluate our methods on real-world datasets, demonstrating how useful constraints about groups can be harnessed from a crowd of non-experts. Our methods can rival supervised models trained on many true labels, and can obtain considerably better results from the crowd than a standard label-collection process (for a lower price). By collecting rough guesses on groups of instances and using machine learning to infer the individual labels, our lightweight framework is able to address core crowdsourcing challenges and train machine learning models in a cost-effective way.