Dialog system developers need high-quality data to train, fine-tune and assess their systems. They often use crowdsourcing for this since it provides large quantities of data from many workers. However, the data may not be of sufficiently good quality. This can be due to the way that the requester presents a task and how they interact with the workers. This paper introduces DialCrowd 2.0 to help requesters obtain higher quality data by, for example, presenting tasks more clearly and facilitating effective communication with workers. DialCrowd 2.0 guides developers in creating improved Human Intelligence Tasks (HITs) and is directly applicable to the workflows used currently by developers and researchers.
@article{arxiv.2207.12551,
title = {DialCrowd 2.0: A Quality-Focused Dialog System Crowdsourcing Toolkit},
author = {Jessica Huynh and Ting-Rui Chiang and Jeffrey Bigham and Maxine Eskenazi},
journal= {arXiv preprint arXiv:2207.12551},
year = {2022}
}