Learning From Crowds Using Graph Neural Networks With Attention Mechanism

Jing Zhang, Ming Wu, Zeyi Sun, Cangqi Zhou

Abstract: Crowdsourcing has been playing an essential role in machine learning since it can obtain a large number of labels in an economical and fast manner for training increasingly complex learning models. However, the application of crowdsourcing learning still faces several challenges such as the low quality of crowd labels and the urgent requirement for learning models adapting to the label noises. There have been many studies focusing on truth inference algorithms to improve the quality of labels obtained by crowdsourcing. Comparably, end-to-end predictive model learning in crowdsourcing scenarios, especially using cutting-edge deep learning techniques, is still in its infant stage. In this paper, we propose a novel graph convolutional network-based framework, namely CGNNAT, which models the correlation of instances by combining the GCN model with an attention mechanism to learn more representative node embeddings for a better understanding of the bias tendency of crowd workers. Furthermore, a specific projection processing layer is employed in CGNNAT to model the reliability of each crowd worker, which makes the model an end-to-end neural network directly trained by noisy crowd labels. Experimental results on several real-world and synthetic datasets show that the proposed CGNNAT outperforms state-of-the-art and classical methods in terms of label prediction.