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A Majority of Wrongs Doesn't Make It Right -On Crowdsourcing Quality for Skewed Domain Tasks

机译:多数错误并不能解决问题-偏领域任务的众包质量

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Today, crowdsourcing has emerged as a promising paradigm for annotating, structuring, and managing Web data. Still, as long as the problem of the crowd workers' trustworthiness in terms of result quality is not essentially solved, all these efforts remain doubtful. Therefore, in this paper we look at today's dominant quality assurance techniques and investigate how they cope with Web data, i.e. typical long-tail distributions, making it easy for strategic spammers to guess the prevalent answers and thus to go undetected. We provide a thorough theoretical analysis, quantifying the success of different methods on such skewed domains by means of test theory and show their individual weaknesses. Exploiting our case study analysis, we propose a simple privacy-preserving, task-agnostic model to improve test reliability, while actually decreasing overhead costs for quality assurance. Finally, we show the stability of our method for even higher numbers of spammers in controlled crowdsourcing experiments.
机译:如今,众包已经成为注释,构建和管理Web数据的有希望的范例。但是,只要不能从本质上解决人群工人对结果质量的信任度问题,所有这些努力仍然值得怀疑。因此,在本文中,我们着眼于当今主流的质量保证技术,并研究它们如何处理Web数据(即典型的长尾分布),从而使战略垃圾邮件发送者更容易猜测普遍的答案,从而使其无法被发现。我们提供了透彻的理论分析,通过检验理论对这种偏斜域上不同方法的成功进行了量化,并展示了它们各自的弱点。利用我们的案例分析,我们提出了一个简单的隐私保护,与任务无关的模型,以提高测试的可靠性,同时实际上减少了用于质量保证的开销成本。最后,我们在受控众包实验中证明了我们方法对更多垃圾邮件发送者的稳定性。

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