DialCrowd 2.0: A Quality-Focused Dialog System Crowdsourcing Toolkit

July 25, 2022 ยท Declared Dead ยท ๐Ÿ› International Conference on Language Resources and Evaluation

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Authors Jessica Huynh, Ting-Rui Chiang, Jeffrey Bigham, Maxine Eskenazi arXiv ID 2207.12551 Category cs.CL: Computation & Language Citations 6 Venue International Conference on Language Resources and Evaluation Last Checked 5 months ago
Abstract
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.
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