  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.
