  Discourse coherence is strongly associated with text quality, making it
important to natural language generation and understanding. Yet existing models
of coherence focus on measuring individual aspects of coherence (lexical
overlap, rhetorical structure, entity centering) in narrow domains.
  In this paper, we describe domain-independent neural models of discourse
coherence that are capable of measuring multiple aspects of coherence in
existing sentences and can maintain coherence while generating new sentences.
We study both discriminative models that learn to distinguish coherent from
incoherent discourse, and generative models that produce coherent text,
including a novel neural latent-variable Markovian generative model that
captures the latent discourse dependencies between sentences in a text.
  Our work achieves state-of-the-art performance on multiple coherence
evaluations, and marks an initial step in generating coherent texts given
discourse contexts.
