---
title: "Topic Modelling Meets Deep Neural Networks: A Survey"
authors:
  - "He Zhao"
  - "Dinh Phung"
  - "Viet Huynh"
  - "Yuan Jin"
  - "Lan Du"
  - "Wray Buntine"
doi: "10.24963/ijcai.2021/638"
url: "https://ijcai.org/proceedings/2021/638"
pdf: "https://ijcai.org/proceedings/2021/0638.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4713-4720"
track: "Survey Track"
keywords:
  - "Machine learning: General"
  - "Natural language processing: General"
  - "Knowledge representation and reasoning: General"
---

# Topic Modelling Meets Deep Neural Networks: A Survey

He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, Wray Buntine

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4713-4720. DOI: [10.24963/ijcai.2021/638](https://doi.org/10.24963/ijcai.2021/638)

## Abstract

Topic modelling has been a successful technique for text analysis for almost twenty years. When topic modelling met deep neural networks, there emerged a new and increasingly popular research area, neural topic models, with nearly a hundred models developed and a wide range of applications in neural language understanding such as text generation, summarisation and language models. There is a need to summarise research developments and discuss open problems and future directions. In this paper, we provide a focused yet comprehensive overview of neural topic models for interested researchers in the AI community, so as to facilitate them to navigate and innovate in this fast-growing research area. To the best of our knowledge, ours is the first review on this specific topic.