Automated plot generation is the challenge of generating a sequence of events that will be perceived by readers as the plot of a coherent story. Traditional symbolic planners plan a story from a goal state and guarantee logical causal plot coherence but rely on a library of hand-crafted actions with their preconditions and effects. This closed world setting limits the length and diversity of what symbolic planners can generate. On the other hand, pre-trained neural language models can generate stories with great diversity, while being generally incapable of ending a story in a specified manner and can have trouble maintaining coherence. In this paper, we present an approach to story plot generation that unifies causal planning with neural language models. We propose to use commonsense knowledge extracted from large language models to recursively expand a story plot in a backward chaining fashion. Specifically, our system infers the preconditions for events in the story and then events that will cause those conditions to become true. We performed automatic evaluation to measure narrative coherence as indicated by the ability to answer questions about whether different events in the story are causally related to other events. Results indicate that our proposed method produces more coherent plotlines than several strong baselines.
翻译:自动地块生成是产生一系列事件的挑战,这些事件会被读者视为一个连贯故事的情节。传统的象征性规划者从一个目标状态规划一个故事,保证逻辑因果的情节一致性,但依靠一个手工艺行动图书馆及其先决条件和效果。这个封闭的世界限制了象征性规划者所能创造的事件的长度和多样性。另一方面,预先培训的神经语言模型可以产生非常多样的故事,而通常无法以特定的方式结束一个故事,并可能难以保持一致性。在本文中,我们提出了一个故事情节生成方法,将因果规划与神经语言模型统一起来。我们提议使用从大语言模型中提取的常识性知识,以后向链式的方式反复扩展一个故事情节。具体地说,我们的系统推理出故事中的事件的先决条件,然后是导致这些条件成为真实的事件。我们进行了自动评估,以衡量叙事的一致性,因为能够回答关于故事中的不同事件是否与其它事件有因果关系的问题。结果显示,我们提出的方法比几个强有力的基线更一致。