论文标题
NGEP：基于图的故事计划框架
NGEP: A Graph-based Event Planning Framework for Story Generation
论文作者
论文摘要
为了提高长期文字的表现，最近的研究利用自动计划的事件结构（即故事情节）来指导故事生成。这样的先前工作主要采用端到端的神经生成模型来预测故事的事件序列。但是，由于幻觉问题，这种一代模型努力确保单独事件的叙事连贯性，此外，由于模型的端到端性质，生成的事件序列通常很难控制。为了应对这些挑战，我们提出了NGEP，这是一个新颖的事件计划框架，通过对自动构造的事件图进行推断并通过神经事件顾问提高概括能力来生成事件序列。我们在多个标准上进行了一系列实验，结果表明，我们的基于图的神经框架的表现优于最新的事件计划计划方法，考虑到事件序列产生的性能以及对故事产生的下游任务的有效性。
To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i.e. storylines) to guide story generation. Such prior works mostly employ end-to-end neural generation models to predict event sequences for a story. However, such generation models struggle to guarantee the narrative coherence of separate events due to the hallucination problem, and additionally the generated event sequences are often hard to control due to the end-to-end nature of the models. To address these challenges, we propose NGEP, an novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor. We conduct a range of experiments on multiple criteria, and the results demonstrate that our graph-based neural framework outperforms the state-of-the-art (SOTA) event planning approaches, considering both the performance of event sequence generation and the effectiveness on the downstream task of story generation.
