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Label similarity-based weighted soft majority voting and pairing for crowdsourcing

机译：基于标签相似性的加权软多数投票和挤成众包配对

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摘要

Crowdsourcing services provide an efficient and relatively inexpensive approach to obtain substantial amounts of labeled data by employing crowd workers. It is obvious that the labeling qualities of crowd workers directly affect the quality of the labeled data. However, existing label aggregation strategies seldom consider the differences in the quality of workers labeling different instances. In this paper, we argue that a single worker may even have different labeling qualities on different instances. Based on this premise, we propose four new strategies by assigning different weights to workers when labeling different instances. In our proposed strategies, we first use the similarity among worker labels to estimate the specific quality of the worker on different instances, and then we build a classifier to estimate the overall quality of the worker across all instances. Finally, we combine these two qualities to define the weight of the worker labeling a particular instance. Extensive experimental results show that our proposed strategies significantly outperform other existing state-of-the-art label aggregation strategies.
机译：众包服务提供了一种高效且相对便宜的方法，通过雇用人群工人获得大量标记的数据。很明显，人群工人的标签质量直接影响标记数据的质量。但是，现有的标签聚合策略很少考虑标签不同实例的工人质量的差异。在本文中，我们认为单个工人甚至可以在不同的情况下具有不同的标记品质。基于这一前提，我们在标记不同实例时为工人分配不同权重的新策略提出了四种新策略。在我们提出的策略中，我们首先使用工作者标签之间的相似性来估计不同实例的工人的具体质量，然后我们构建一个分类器来估计所有实例的工作人员的整体质量。最后，我们结合了这两个品质来定义工作人员的重量标记特定实例。广泛的实验结果表明，我们的拟议策略显着优于其他现有的最先进的标签聚合策略。

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