  In this work, we propose BertGCN, a model that combines large scale
pretraining and transductive learning for text classification. BertGCN
constructs a heterogeneous graph over the dataset and represents documents as
nodes using BERT representations. By jointly training the BERT and GCN modules
within BertGCN, the proposed model is able to leverage the advantages of both
worlds: large-scale pretraining which takes the advantage of the massive amount
of raw data and transductive learning which jointly learns representations for
both training data and unlabeled test data by propagating label influence
through graph convolution. Experiments show that BertGCN achieves SOTA
performances on a wide range of text classification datasets. Code is available
at https://github.com/ZeroRin/BertGCN.
