  Coherence across multiple turns is a major challenge for state-of-the-art
dialogue models. Arguably the most successful approach to automatically
learning text coherence is the entity grid, which relies on modelling patterns
of distribution of entities across multiple sentences of a text. Originally
applied to the evaluation of automatic summaries and the news genre, among its
many extensions, this model has also been successfully used to assess dialogue
coherence. Nevertheless, both the original grid and its extensions do not model
intents, a crucial aspect that has been studied widely in the literature in
connection to dialogue structure. We propose to augment the original grid
document representation for dialogue with the intentional structure of the
conversation. Our models outperform the original grid representation on both
text discrimination and insertion, the two main standard tasks for coherence
assessment across three different dialogue datasets, confirming that intents
play a key role in modelling dialogue coherence.
