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.- Anthology ID:
- 2020.acl-main.73
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 800–806
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.73
- DOI:
- 10.18653/v1/2020.acl-main.73
- Cite (ACL):
- Masaru Isonuma, Junichiro Mori, Danushka Bollegala, and Ichiro Sakata. 2020. Tree-Structured Neural Topic Model. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 800–806, Online. Association for Computational Linguistics.
- Cite (Informal):
- Tree-Structured Neural Topic Model (Isonuma et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.73.pdf