Story generation, namely generating a reasonable story from a leading
context, is an important but challenging task. In spite of the success in
modeling fluency and local coherence, existing neural language generation
models (e.g., GPT-2) still suffer from repetition, logic conflicts, and lack of
long-range coherence in generated stories. We conjecture that this is because
of the difficulty of associating relevant commonsense knowledge, understanding
the causal relationships, and planning entities and events with proper temporal
order. In this paper, we devise a knowledge-enhanced pretraining model for
commonsense story generation. We propose to utilize commonsense knowledge from
external knowledge bases to generate reasonable stories. To further capture the
causal and temporal dependencies between the sentences in a reasonable story,
we employ multi-task learning which combines a discriminative objective to
distinguish true and fake stories during fine-tuning. Automatic and manual
evaluation shows that our model can generate more reasonable stories than
state-of-the-art baselines, particularly in terms of logic and global
coherence.

本文针对故事生成中存在的重复、逻辑冲突和长距离一致性缺乏等问题，提出了一种基于知识增强预训练模型的通用故事生成方法。通过利用外部知识库中的常识知识来生成合理的故事，并采用多任务学习的方法来捕捉合理故事中句子之间的因果关系和时间依赖关系，从而在逻辑和整体一致性方面比其他最先进的模型有更好的表现。