  Predicting the subsequent event for an existing event context is an important
but challenging task, as it requires understanding the underlying relationship
between events. Previous methods propose to retrieve relational features from
event graph to enhance the modeling of event correlation. However, the sparsity
of event graph may restrict the acquisition of relevant graph information, and
hence influence the model performance. To address this issue, we consider
automatically building of event graph using a BERT model. To this end, we
incorporate an additional structured variable into BERT to learn to predict the
event connections in the training process. Hence, in the test process, the
connection relationship for unseen events can be predicted by the structured
variable. Results on two event prediction tasks: script event prediction and
story ending prediction, show that our approach can outperform state-of-the-art
baseline methods.
