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Generating long and coherent text is an important but challenging task,particularly for open-ended language generation tasks such as story generation.Despite the success in modeling intra-sentence coherence, existing generationmodels (e.g., BART) still struggle to maintain a coherent event sequencethroughout the generated text. We conjecture that this is because of thedifficulty for the decoder to capture the high-level semantics and discoursestructures in the context beyond token-level co-occurrence. In this paper, wepropose a long text generation model, which can represent the prefix sentencesat sentence level and discourse level in the decoding process. To this end, wepropose two pretraining objectives to learn the representations by predictinginter-sentence semantic similarity and distinguishing between normal andshuffled sentence orders. Extensive experiments show that our model cangenerate more coherent texts than state-of-the-art baselines.

Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence