  Within the context of event modeling and understanding, we propose a new
method for neural sequence modeling that takes partially-observed sequences of
discrete, external knowledge into account. We construct a sequential neural
variational autoencoder, which uses Gumbel-Softmax reparametrization within a
carefully defined encoder, to allow for successful backpropagation during
training. The core idea is to allow semi-supervised external discrete knowledge
to guide, but not restrict, the variational latent parameters during training.
Our experiments indicate that our approach not only outperforms multiple
baselines and the state-of-the-art in narrative script induction, but also
converges more quickly.
