Our contributions are four-fold: (1) We propose a MTL approach to coherence assessment and compare it against a number of baselines.
we experimentally demonstrate that such a framework allows us to exploit more effectively the inter-dependencies between the two prediction tasks and achieve state-of-the-art results in predicting document-level coherence; (2) we assess the extent to which the information encoded in the network generalizes to different domains and prediction tasks, and demonstrate the effectiveness of our approach not only on standard binary evaluation tasks on the wall Street Journal (wSJ), but also on more realistic tasks involving the prediction of varying degrees of coherence in people's everyday writing; (3) to existing work that has only investigated the impact of a specific set of grammatical roles (i.e., subject and object) on coherence, we instead investigate a large set of GR types, and train the model to predict the type of role dependents participate in.