HVAE: A deep generative model via hierarchical variational auto-encoder for multi-view document modeling

With the widespread development of the internet, multi-view text documents have become increasingly common, which has led to extensive research on multi-view text doc-ument modeling. As opposed to traditional single-view document modeling, which treats each document independently and learns each document as a single topic representation, the views of multi-view text documents have complicated correlation relationships that include both the global and local underlying topical information. In this study, we intro-duce a deep generative model for multi-view document modeling known as Hierarchical Variational Auto -Encoder (HVAE), which combines the advantages of the probability gen-erative model for learning interpretable latent information and the deep neural network for efficient parameter inference. Specifically, a set of hierarchical topic representations is learned for each multi-view document to capture the document-level global topical information and view-level local topical information for each view. A two-level hierarchical topic inference network is investigated as the encoder network of HVAE, which is designed using an aligned variational auto-encoder, to learn the hierarchical topic representations. Subsequently, multi-view documents are generated through a two-layered generation net-work, considering both the view-level local and document-level global topic representa-tions. Experiments on three real datasets of different scales for various tasks demonstrate the satisfactory results of the proposed method.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

With the development of the internet, multi-view text documents have become common and research on multi-view document modeling has increased. Traditional single-view document modeling treats each document independently, while multi-view text documents have complex correlations. This study introduces a deep generative model called Hierarchical Variational Auto-Encoder (HVAE) that combines the advantages of probability generative models and deep neural networks. The proposed method successfully captures both global and local topical information in multi-view documents.