@inproceedings{isonuma-etal-2020-tree,
    title = "{T}ree-{S}tructured {N}eural {T}opic {M}odel",
    author = "Isonuma, Masaru  and
      Mori, Junichiro  and
      Bollegala, Danushka  and
      Sakata, Ichiro",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.acl-main.73/",
    doi = "10.18653/v1/2020.acl-main.73",
    pages = "800--806",
    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."
}