  In this paper, we investigate the use of discourse-aware rewards with
reinforcement learning to guide a model to generate long, coherent text. In
particular, we propose to learn neural rewards to model cross-sentence ordering
as a means to approximate desired discourse structure. Empirical results
demonstrate that a generator trained with the learned reward produces more
coherent and less repetitive text than models trained with cross-entropy or
with reinforcement learning with commonly used scores as rewards.
