Dirichlet belief networks for topic structure learning
This work addresses the need for interpretable topic hierarchies in natural language processing, offering a flexible module that can enhance existing topic models, though it is incremental in building upon deep architectures for topic modeling.
The authors tackled the problem of learning hierarchical topic structures in topic models by proposing a multi-layer generative process for word distributions, which improved modeling accuracy and interpretability across various text corpora.
Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.