Scripts are structured sequences of events together with the participants,
which are extracted from the texts.Script event prediction aims to predict the
subsequent event given the historical events in the script. Two kinds of
information facilitate this task, namely, the event-level information and the
script-level information. At the event level, existing studies view an event as
a verb with its participants, while neglecting other useful properties, such as
the state of the participants. At the script level, most existing studies only
consider a single event sequence corresponding to one common protagonist. In
this paper, we propose a Transformer-based model, called MCPredictor, which
integrates deep event-level and script-level information for script event
prediction. At the event level, MCPredictor utilizes the rich information in
the text to obtain more comprehensive event semantic representations. At the
script-level, it considers multiple event sequences corresponding to different
participants of the subsequent event. The experimental results on the
widely-used New York Times corpus demonstrate the effectiveness and superiority
of the proposed model.

本文提出了一个基于 Transformer 模型的 MCPredictor，将深度的事件级别信息和剧本级别信息整合起来，实现对剧本事件的预测，实验结果在广泛使用的《纽约时报》语料库上证明了所提出的模型的有效性和优越性。

将深度事件级别和脚本级别信息集成用于脚本事件预测

Integrating Deep Event-Level and Script-Level Information for Script  Event Prediction

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。