  Models encapsulating narrative schema knowledge have proven to be useful for
a range of event-related tasks, but these models typically do not engage with
temporal relationships between events. We present a a BART-based conditional
generation model capable of capturing event cooccurrence as well as temporality
of event sequences. This single model can address both temporal ordering,
sorting a given sequence of events into the order they occurred, and event
infilling, predicting new events which fit into a temporally-ordered sequence
of existing ones. Our model is trained as a denoising autoencoder: we take
temporally-ordered event sequences, shuffle them, delete some events, and then
attempting to recover the original event sequence. In this fashion, the model
learns 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.
