Script is a kind of structured knowledge extracted from texts, which contains a sequence of events. Based on such knowledge, script event prediction aims to predict the subsequent event. To do so, two aspects should be considered for events, namely, event description (i.e., what the events should contain) and event encoding (i.e., how they should be encoded). Most existing methods describe an event by a verb together with only a few core arguments (i.e., subject, object, and indirect object), which are not precise. In addition, existing event encoders are limited to a fixed number of arguments, which are not flexible to deal with extra information. Thus, in this paper, we propose the Rich Event Prediction (REP) framework for script event prediction. Fundamentally, it is based on the proposed rich event description, which enriches the existing ones with three kinds of important information, namely, the senses of verbs, extra semantic roles, and types of participants. REP contains an event extractor to extract such information from texts. Based on the extracted rich information, a predictor then selects the most probable subsequent event. The core component of the predictor is a transformer-based event encoder to flexibly deal with an arbitrary number of arguments. Experimental results on the widely used Gigaword Corpus show the effectiveness of the proposed framework.

翻译：脚本是一种从文本中提取的结构化知识, 包含一系列事件。 基于这种知识, 脚本事件预测旨在预测随后的事件。 要做到这一点, 必须考虑事件的两个方面, 即事件描述( 事件应包含什么) 和事件编码( 如何编码它们)。 大多数现有方法用动词来描述事件, 并且只有少数核心论点( 主题、 对象和间接对象), 这些论点并不精确 。 此外, 现有的事件编码器只限于固定数量的参数, 无法灵活处理额外信息 。 因此, 在本文中, 我们提出用于脚本事件预测的富集事件预测( 即, 事件预测( REP) 框架。 基本而言, 它基于拟议的丰富事件描述, 以三种重要信息丰富现有事件, 即动词感、 额外语义作用 和参与者的种类 。 REP 包含一个从文本中提取这类信息的活动框架。 在提取的丰富信息时, 预告器会后, 将选择一个最精确的变动的 Coriaal 。