Hierarchical Intention Enhanced Network for Automatic Dialogue Coherence Assessment

Dialogue coherence across multiple turns is still an open challenge. The entity grid model is arguably the most popular approach for coherence modeling. However, it heavily relies on the distribution of entities across adjacent sentences but ignores the emotional context embedded in non-entity text and fails to model long dependencies between speech intentions. These limitations become even more severe when applied to dialogue domain since sentences in dialogue are short, informal and colloquial, thereby, less entities could be extracted and less coherence information could be expressed in these grids. To address the limitations of entity gird methods and incorporate the structure knowledge of dialogue, we propose a new neural network architecture, Hierarchical Intention Enhance Network, to integrate semantic context and speech intention in both utterance and dialogue levels to hierarchically model the global coherence without any entity grids. Our proposed model outperforms the state-of-the-art entity-grid based coherence model on text discrimination task by 17.13% increase in accuracy, confirming the effectiveness of our hierarchical modeling in dialogue context and the crucial importance of intention information in dialogue coherence assessment.