Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification

Sun, Zhongtian; Harit, Anoushka; Cristea, Alexandra I.; Yu, Jialin; Shi, Lei; Al Moubayed, Noura

Abstract

Graph neural networks (GNNs) have attracted extensive interest in text classification tasks due to their expected superior performance in representation learning. However, most existing studies adopted the same semi-supervised learning setting as the vanilla Graph Convolution Network (GCN), which requires a large amount of labelled data during training and thus is less robust when dealing with large-scale graph data with fewer labels. Additionally, graph structure information is normally captured by direct information aggregation via network schema and is highly dependent on correct adjacency information. Therefore, any missing adjacency knowledge may hinder the performance. Addressing these problems, this paper thus proposes a novel method to learn a graph structure, NC-HGAT, by expanding a state-of-the-art self-supervised heterogeneous graph neural network model (HGAT) with simple neighbour contrastive learning. The new NC-HGAT considers the graph structure information from heterogeneous graphs with multilayer perceptrons (MLPs) and delivers consistent results, despite the corrupted neighbouring connections. Extensive experiments have been implemented on four benchmark short-text datasets. The results demonstrate that our proposed model NC-HGAT significantly outperforms state-of-the-art methods on three datasets and achieves competitive performance on the remaining dataset.

Citation

Sun, Z., Harit, A., Cristea, A. I., Yu, J., Shi, L., & Al Moubayed, N. (2022, July). Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2022 International Joint Conference on Neural Networks (IJCNN)
• Start Date: Jul 18, 2022
• End Date: Jul 23, 2022
• Acceptance Date: Apr 26, 2022
• Online Publication Date: Sep 30, 2022
• Publication Date: 2022
• Deposit Date: Aug 31, 2022
• Publicly Available Date: Sep 1, 2022
• Series ISSN: 2161-4393,2161-4407
• DOI: https://doi.org/10.1109/ijcnn55064.2022.9892257
• Public URL: https://durham-repository.worktribe.com/output/1136347