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Despite the recent advances in applying pre-trained language models togenerate high-quality texts, generating long passages that maintain long-rangecoherence is yet challenging for these models. In this paper, we proposeDiscoDVT, a discourse-aware discrete variational Transformer to tackle theincoherence issue. DiscoDVT learns a discrete variable sequence that summarizesthe global structure of the text and then applies it to guide the generationprocess at each decoding step. To further embed discourse-aware informationinto the discrete latent representations, we introduce an auxiliary objectiveto model the discourse relations within the text. We conduct extensiveexperiments on two open story generation datasets and demonstrate that thelatent codes learn meaningful correspondence to the discourse structures thatguide the model to generate long texts with better long-range coherence.

DiscoDVT: Generating Long Text with Discourse-Aware Discrete Variational Transformer