  Models of narrative schema knowledge have proven useful for a range of
event-related tasks, but they typically do not capture the temporal
relationships between events. We propose a single model that addresses both
temporal ordering, sorting given events into the order they occurred, and event
infilling, predicting new events which fit into an existing temporally-ordered
sequence. We use a BART-based conditional generation model that can capture
both temporality and common event co-occurrence, meaning it can be flexibly
applied to different tasks in this space. Our model is trained as a denoising
autoencoder: we take temporally-ordered event sequences, shuffle them, delete
some events, and then attempt to recover the original event sequence. This task
teaches the model to make inferences given incomplete knowledge about the
events in an underlying scenario. On the temporal ordering task, we show that
our model is able to unscramble event sequences from existing datasets without
access to explicitly labeled temporal training data, outperforming both a
BERT-based pairwise model and a BERT-based pointer network. On event infilling,
human evaluation shows that our model is able to generate events that fit
better temporally into the input events when compared to GPT-2 story completion
models.
