  To extract the structured representations of open-domain events, Bayesian
graphical models have made some progress. However, these approaches typically
assume that all words in a document are generated from a single event. While
this may be true for short text such as tweets, such an assumption does not
generally hold for long text such as news articles. Moreover, Bayesian
graphical models often rely on Gibbs sampling for parameter inference which may
take long time to converge. To address these limitations, we propose an event
extraction model based on Generative Adversarial Nets, called
Adversarial-neural Event Model (AEM). AEM models an event with a Dirichlet
prior and uses a generator network to capture the patterns underlying latent
events. A discriminator is used to distinguish documents reconstructed from the
latent events and the original documents. A byproduct of the discriminator is
that the features generated by the learned discriminator network allow the
visualization of the extracted events. Our model has been evaluated on two
Twitter datasets and a news article dataset. Experimental results show that our
model outperforms the baseline approaches on all the datasets, with more
significant improvements observed on the news article dataset where an increase
of 15\% is observed in F-measure.
