Abstract
We address the task of assessing discourse coherence, an aspect of text quality that is essential for many NLP tasks, such as summarization and language assessment. We propose a
hierarchical neural network trained in a multitask fashion that learns to predict a documentlevel coherence score (at the network’s top layers) along with word-level grammatical roles
(at the bottom layers), taking advantage of inductive transfer between the two tasks. We assess the extent to which our framework generalizes to different domains and prediction
tasks, and demonstrate its effectiveness not
only on standard binary evaluation coherence
tasks, but also on real-world tasks involving
the prediction of varying degrees of coherence,
achieving a new state of the art.