Tree-Structured Neural Topic Model

Isonuma, Masaru, Mori, Junichiro, Bollegala, Danushka ORCID: 0000-0003-4476-7003 and Sakata, Ichiro
(2020) Tree-Structured Neural Topic Model. In: Annual Conference of the Association for Computational Linguistics, 2020-7-5 - 2020-7-10, Seattle, USA.

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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.

Item Type: Conference or Workshop Item (Unspecified)
Depositing User: Symplectic Admin
Date Deposited: 13 May 2020 10:23
Last Modified: 19 Mar 2023 00:27
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