Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks
Wei Liu, Xiyan Fu, Michael Strube · 2023
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.