  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.
