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, a label aggregation model that infers the true label from noisy crowdsourced labels is required. In this article, 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.

中文翻译：

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利用具有潜在工作者/任务相关信息的异构图神经网络进行众包中的标签聚合

众包因其方便地从非专家而不是专家那里收集标签而备受关注。然而，由于来自非专家的高水平噪音，需要一个从嘈杂的众包标签中推断出真实标签的标签聚合模型。在本文中，我们提出了一种基于图神经网络的新框架，用于聚合人群标签。我们在工作人员和任务之间构建了一个异构图，并派生了一个新的图神经网络来学习节点的表示和真实标签。此外，我们通过在图神经网络中添加同质注意层来利用相同类型的节点（工作者或任务）之间的未知潜在交互。13 个真实世界数据集的实验结果显示出优于最先进模型的性能。