Graph convolutional networks (GCNs) and very recently graph recurrent networks (GRNs) have been used to model graph structures in NLP tasks, such as semantic role labeling , machine translation (Bastings et al., 2017), text generation ), text representation  and semantic parsing (Xu et al., 2018b,a).
 use GRN to represent raw sentences by building a graph structure of neighboring words and a sentence-level node, showing that the encoder outperforms BiLSTMs and Transformer (Vaswani et al., 2017) on classification and sequence labeling tasks; Song et al.