Semantic-based Bidirectional Adversarial Neural Topic Model

The topic models can effectively reduce the dimensionality of document representation. In recent years, Generative Adversarial Networks (GANs) have begun to be applied to topic models, but most researches do not fully consider the documents’ context information, leading to the fact that the generated results of the encoder networks cannot represent the real data distribution. To this end, we propose a novel topic model named Semantic-based Bidirectional Adversarial Neural Topic Model (SNTM), which introduces semantic information into Bidirectional Generative Adversarial Networks (BiGAN) by adding the word embedding and BiLSTM-Attention mechanism. This improvement makes the encoder network in our model generate a distribution closer to the real document-topic distribution. Furthermore, we also propose a topic difference evaluation indicator, which more comprehensively evaluates the quality of the generated topics. The experimental results on different datasets show that SNTM performs better than the baseline topic model.