Modeling Structural Similarities between Documents for Coherence
  Assessment with Graph Convolutional Networks
Wei Liu Xiyan Fu Michael Strube
Coherence is an important aspect of text quality, and various approaches have
been applied to coherence modeling. However, existing methods solely focus on a
single document's coherence patterns, ignoring the underlying correlation
between documents. We investigate a GCN-based coherence model that is capable
of capturing structural similarities between documents. Our model first creates
a graph structure for each document, from where we mine different subgraph
patterns. We then construct a heterogeneous graph for the training corpus,
connecting documents based on their shared subgraphs. Finally, a GCN is applied
to the heterogeneous graph to model the connectivity relationships. We evaluate
our method on two tasks, assessing discourse coherence and automated essay
scoring. Results show that our GCN-based model outperforms all baselines,
achieving a new state-of-the-art on both tasks.