Tree-Structured Neural Topic Model

Masaru Isonuma, Junichiro Mori, Danushka Bollegala, Ichiro Sakata

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Information Retrieval and Text Mining Short Paper

Session 1B: Jul 6 (06:00-07:00 GMT)
Session 2A: Jul 6 (08:00-09:00 GMT)
Abstract: This paper presents a tree-structured neural topic model, which has a topic distribution over a tree with an infinite number of branches. Our model parameterizes an unbounded ancestral and fraternal topic distribution by applying doubly-recurrent neural networks. With the help of autoencoding variational Bayes, our model improves data scalability and achieves competitive performance when inducing latent topics and tree structures, as compared to a prior tree-structured topic model (Blei et al., 2010). This work extends the tree-structured topic model such that it can be incorporated with neural models for downstream tasks.
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