Discourse-Aware neural rewards for coherent text generation

Abstract

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 crossentropy or with reinforcement learning with commonly used scores as rewards.