论文标题
利用潜在工人/任务相关信息来利用异构图神经网络，用于众包的标签聚合
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
论文作者
论文摘要
众包为其便利而不是从专家那里收集标签，引起了很多关注。但是，由于非专家的噪声水平很高，需要通过合并源信誉来了解真实标签的聚合模型。在本文中，我们提出了一个基于图形神经网络的新框架，用于汇总人群标签。我们在工人和任务之间构建一个异质图，并得出一个新的图神经网络，以了解节点和真实标签的表示。此外，我们通过在图神经网络中添加同质的注意力层来利用相同类型的节点（工人或任务）之间未知的潜在相互作用。 13个现实世界数据集的实验结果表现出优于最先进模型的性能。
Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-experts, an aggregation model that learns the true label by incorporating the source credibility is required. In this paper, we propose a novel framework based on graph neural networks for aggregating crowd labels. We construct a heterogeneous graph between workers and tasks and derive a new graph neural network to learn the representations of nodes and the true labels. Besides, we exploit the unknown latent interaction between the same type of nodes (workers or tasks) by adding a homogeneous attention layer in the graph neural networks. Experimental results on 13 real-world datasets show superior performance over state-of-the-art models.
