Title: Neural Variational Sparse Topic Model. Abstract: Effectively inferring discriminative and coherent latent topics of short texts is a critical task for many real world applications. Nevertheless, the task has been proven to be a great challenge for traditional topic models due to the data sparsity problem induced by the characteristics of short texts. Moreover, the complex inference algorithm also become a bottleneck for these traditional models to rapidly explore variations. In this paper, we propose a novel model called Neural Variational Sparse Topic Model (NVSTM) based on a sparsity-enhanced topic model named Sparse Topical Coding (STC). In the model, the auxiliary word embeddings are utilized to improve the generation of representations. The Variational Autoencoder (VAE) approach is applied to inference the model efficiently, which makes the model easy to explore extensions for its black-box inference process. Experimental results onWeb Snippets, 20Newsgroups, BBC and Biomedical datasets show the effectiveness and efficiency of the model.