  There is a small but growing body of research on statistical scripts, models
of event sequences that allow probabilistic inference of implicit events from
documents. These systems operate on structured verb-argument events produced by
an NLP pipeline. We compare these systems with recent Recurrent Neural Net
models that directly operate on raw tokens to predict sentences, finding the
latter to be roughly comparable to the former in terms of predicting missing
events in documents.
