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Despite advances in generating fluent texts, existing pretraining models tendto attach incoherent event sequences to involved entities when generatingnarratives such as stories and news. We conjecture that such issues result fromrepresenting entities as static embeddings of superficial words, whileneglecting to model their ever-changing states, i.e., the information theycarry, as the text unfolds. Therefore, we extend the Transformer model todynamically conduct entity state updates and sentence realization for narrativegeneration. We propose a contrastive framework to learn the staterepresentations in a discrete space, and insert additional attention layersinto the decoder to better exploit these states. Experiments on two narrativedatasets show that our model can generate more coherent and diverse narrativesthan strong baselines with the guidance of meaningful entity states.

Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework