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Coherence is an important aspect of text quality, and various approaches havebeen applied to coherence modeling. However, existing methods solely focus on asingle document's coherence patterns, ignoring the underlying correlationbetween documents. We investigate a GCN-based coherence model that is capableof capturing structural similarities between documents. Our model first createsa graph structure for each document, from where we mine different subgraphpatterns. We then construct a heterogeneous graph for the training corpus,connecting documents based on their shared subgraphs. Finally, a GCN is appliedto the heterogeneous graph to model the connectivity relationships. We evaluateour method on two tasks, assessing discourse coherence and automated essayscoring. Results show that our GCN-based model outperforms all baselines,achieving a new state-of-the-art on both tasks.

Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks