Instead of providing an analytic approximation, AEM uses a discriminator network to discriminate between the reconstructed documents from latent events and the original input documents. This essentially helps the generator to construct a more realistic document from a random noise drawn from a Dirichlet distribution. Due to the flexibility of neural networks, the generator is capable of learning complicated nonlinear distributions. And the supervision signal provided by the discriminator will help generator to capture the event-related patterns. 
 Question: How does this model overcome the assumption that all words in a document are generated from a single event?
flexibility of neural networks, the generator is capable of learning complicated nonlinear distributions supervision signal provided by the discriminator will help generator to capture the event-related patterns