Continual Graph Convolutional Network for Text Classification
This addresses the problem of real-time text classification for applications like public opinion analysis, offering a novel approach to handle streaming data, though it builds incrementally on existing GCN methods.
The paper tackles the challenge of deploying graph convolutional networks (GCNs) for text classification in online systems with streaming data, proposing a continual GCN model (ContGCN) that achieves an 8.86% performance gain in a 3-month A/B test compared to state-of-the-art methods.
Graph convolutional network (GCN) has been successfully applied to capture global non-consecutive and long-distance semantic information for text classification. However, while GCN-based methods have shown promising results in offline evaluations, they commonly follow a seen-token-seen-document paradigm by constructing a fixed document-token graph and cannot make inferences on new documents. It is a challenge to deploy them in online systems to infer steaming text data. In this work, we present a continual GCN model (ContGCN) to generalize inferences from observed documents to unobserved documents. Concretely, we propose a new all-token-any-document paradigm to dynamically update the document-token graph in every batch during both the training and testing phases of an online system. Moreover, we design an occurrence memory module and a self-supervised contrastive learning objective to update ContGCN in a label-free manner. A 3-month A/B test on Huawei public opinion analysis system shows ContGCN achieves 8.86% performance gain compared with state-of-the-art methods. Offline experiments on five public datasets also show ContGCN can improve inference quality. The source code will be released at https://github.com/Jyonn/ContGCN.