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

Discourse-Aware Neural Rewards for Coherent Text Generation