  The complicated syntax structure of natural language is hard to be explicitly
modeled by sequence-based models. Graph is a natural structure to describe the
complicated relation between tokens. The recent advance in Graph Neural
Networks (GNN) provides a powerful tool to model graph structure data, but
simple graph models such as Graph Convolutional Networks (GCN) suffer from
over-smoothing problem, that is, when stacking multiple layers, all nodes will
converge to the same value. In this paper, we propose a novel Recursive
Graphical Neural Networks model (ReGNN) to represent text organized in the form
of graph. In our proposed model, LSTM is used to dynamically decide which part
of the aggregated neighbor information should be transmitted to upper layers
thus alleviating the over-smoothing problem. Furthermore, to encourage the
exchange between the local and global information, a global graph-level node is
designed. We conduct experiments on both single and multiple label text
classification tasks. Experiment results show that our ReGNN model surpasses
the strong baselines significantly in most of the datasets and greatly
alleviates the over-smoothing problem.
