论文标题
Trueyes：在移动应用程序中利用微型掩体作为机器学习数据集的众包标签
TruEyes: Utilizing Microtasks in Mobile Apps for Crowdsourced Labeling of Machine Learning Datasets
论文作者
论文摘要
在研究和行业中，监督机器学习的日益增长增加了对标记数据集的需求。众包已成为创建数据标签的一种流行方法。但是，从事大量任务会导致工人疲劳，对标签质量产生负面影响。为了解决这个问题，我们介绍了一个协作众包系统Trueyes，从而可以向移动应用程序用户分发微型任务。 Trueyes允许机器学习实践者发布标签任务，移动应用开发人员以集成货币化的任务广告，以及用户来标记数据而不是观看广告。为了评估系统，我们对N = 296名参与者进行了实验。我们的结果表明，标记数据的质量与传统的众包方法相媲美，大多数用户更喜欢任务广告而不是传统广告。我们讨论了系统的扩展，并解决了将来如何将移动广告空间用作生产资源。
The growing use of supervised machine learning in research and industry has increased the need for labeled datasets. Crowdsourcing has emerged as a popular method to create data labels. However, working on large batches of tasks leads to worker fatigue, negatively impacting labeling quality. To address this, we present TruEyes, a collaborative crowdsourcing system, enabling the distribution of micro-tasks to mobile app users. TruEyes allows machine learning practitioners to publish labeling tasks, mobile app developers to integrate task ads for monetization, and users to label data instead of watching advertisements. To evaluate the system, we conducted an experiment with N=296 participants. Our results show that the quality of the labeled data is comparable to traditional crowdsourcing approaches and most users prefer task ads over traditional ads. We discuss extensions to the system and address how mobile advertisement space can be used as a productive resource in the future.
