  Topic models are widely used for thematic structure discovery in text. But
traditional topic models often require dedicated inference procedures for
specific tasks at hand. Also, they are not designed to generate word-level
semantic representations. To address these limitations, we propose a topic
modeling approach based on Generative Adversarial Nets (GANs), called
Adversarial-neural Topic Model (ATM). The proposed ATM models topics with
Dirichlet prior and employs a generator network to capture the semantic
patterns among latent topics. Meanwhile, the generator could also produce
word-level semantic representations. To illustrate the feasibility of porting
ATM to tasks other than topic modeling, we apply ATM for open domain event
extraction. Our experimental results on the two public corpora show that ATM
generates more coherence topics, outperforming a number of competitive
baselines. Moreover, ATM is able to extract meaningful events from news
articles.
