Statistical Scripts are probabilistic models of sequences of events. For
example, a script model might encode the information that the event "Smith
met with the President" should strongly predict the event "Smith spoke to
the President." We present a number of results improving the state of the art
of learning statistical scripts for inferring implicit events. First, we
demonstrate that incorporating multiple arguments into events, yielding a more
complex event representation than is used in previous work, helps to improve a
co-occurrence-based script system's predictive power. Second, we improve on
these results with a Recurrent Neural Network script sequence model which uses a
Long Short-Term Memory component. We evaluate in two ways: first, we evaluate
systems' ability to infer held-out events from documents (the "Narrative
Cloze" evaluation); second, we evaluate novel event inferences by collecting
human judgments.
We propose a number of further extensions to this work. First, we propose a
number of new probabilistic script models leveraging recent advances
in Neural Network training. These include recurrent sequence models with
different hidden unit structure and Convolutional Neural Network models.
Second, we propose integrating more lexical and linguistic information into
events. Third, we propose incorporating discourse relations between spans of
text into event co-occurrence models, either as output by an off-the-shelf
discourse parser or learned automatically. Finally, we propose investigating the
interface between models of event co-occurrence and coreference resolution, in
particular by integrating script information into general coreference
systems.
PhD proposal, Department of Computer Science, The University of Texas at Austin.
