Discourse Relation-Enhanced Neural Coherence Modeling

Discourse coherence theories posit relations between text spans as a key feature of coherent texts. However, existing work on coherence modeling has paid little attention to discourse relations. In this paper, we provide empirical evidence to demonstrate that relation features are correlated with text coherence. Then, we investigate a novel fusion model that uses position-aware attention and a visible matrix to combine text-and relation-based features for coherence assessment. Experimental results on two benchmarks show that our approaches can significantly improve baselines, demonstrating the importance of relation features for coherence modeling.