Script event prediction requires a model to predict the subsequent event
given an existing event context. Previous models based on event pairs or event
chains cannot make full use of dense event connections, which may limit their
capability of event prediction. To remedy this, we propose constructing an
event graph to better utilize the event network information for script event
prediction. In particular, we first extract narrative event chains from large
quantities of news corpus, and then construct a narrative event evolutionary
graph (NEEG) based on the extracted chains. NEEG can be seen as a knowledge
base that describes event evolutionary principles and patterns. To solve the
inference problem on NEEG, we present a scaled graph neural network (SGNN) to
model event interactions and learn better event representations. Instead of
computing the representations on the whole graph, SGNN processes only the
concerned nodes each time, which makes our model feasible to large-scale
graphs. By comparing the similarity between input context event representations
and candidate event representations, we can choose the most reasonable
subsequent event. Experimental results on widely used New York Times corpus
demonstrate that our model significantly outperforms state-of-the-art baseline
methods, by using standard multiple choice narrative cloze evaluation.

本文利用新闻语料库中的叙事事件链构建出叙事事件进化图去更好地利用事件网络信息进行剧本事件预测，并采用规模缩小的图神经网络进行事件交互建模和更好的事件表示学习，实验结果表明我们的模型在标准的多项选择故事勾画评估中显著优于现有的基准方法。