# ATM:Adversarial-neural Topic Model

arXiv: https://arxiv.org/abs/1811.00265 · HTML: https://mlsanity.com/paper/1811.00265/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 23 of 23)

- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Contrastive Learning for Neural Topic Model](https://mlsanity.com/paper/2110.12764/) (2021)
- [Contextualized Topic Coherence Metrics](https://mlsanity.com/paper/2305.14587/) (2023)
- [A Survey on Neural Topic Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2401.15351/) (2024)
- [Topic Modeling as Multi-Objective Contrastive Optimization](https://mlsanity.com/paper/2402.07577/) (2024)
- [Multilingual and Multimodal Topic Modelling with Pretrained Embeddings](https://mlsanity.com/paper/2211.08057/) (2022)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Improving Contextualized Topic Models with Negative Sampling](https://mlsanity.com/paper/2303.14951/) (2023)
- [Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluation](https://mlsanity.com/paper/2502.07352/) (2025)
- [Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class](https://mlsanity.com/paper/2212.09422/) (2022)
- [Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification](https://mlsanity.com/paper/2402.18825/) (2024)
- [Toward Purpose-oriented Topic Model Evaluation enabled by Large Language Models](https://mlsanity.com/paper/2509.07142/) (2025)
- [Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews](https://mlsanity.com/paper/2101.10150/) (2021)
- [Neural Topic Modeling with Deep Mutual Information Estimation](https://mlsanity.com/paper/2203.06298/) (2022)
- [Towards Better Understanding with Uniformity and Explicit Regularization of Embeddings in Embedding-based Neural Topic Models](https://mlsanity.com/paper/2206.07960/) (2022)
- [Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence](https://mlsanity.com/paper/2303.17324/) (2023)
- [Dynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notes](https://mlsanity.com/paper/2312.14180/) (2023)
- [Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning](https://mlsanity.com/paper/2412.17338/) (2024)
- [Variational Gaussian Topic Model with Invertible Neural Projections](https://mlsanity.com/paper/2105.10095/) (2021)
- [What are Public Concerns about ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You](https://mlsanity.com/paper/2309.01522/) (2023)
- [Evaluating Negative Sampling Approaches for Neural Topic Models](https://mlsanity.com/paper/2503.18167/) (2025)
- [Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport](https://mlsanity.com/paper/2604.12663/) (2026)

## Abstract (arXiv)

Topic models are widely used for thematic structure discovery in text. But traditional topic models often require dedicated inference procedures for specific tasks at hand. Also, they are not designed to generate word-level semantic representations. To address these limitations, we propose a topic modeling approach based on Generative Adversarial Nets (GANs), called Adversarial-neural Topic Model (ATM). The proposed ATM models topics with Dirichlet prior and employs a generator network to capture the semantic patterns among latent topics. Meanwhile, the generator could also produce word-level semantic representations. To illustrate the feasibility of porting ATM to tasks other than topic modeling, we apply ATM for open domain event extraction. Our experimental results on the two public corpora show that ATM generates more coherence topics, outperforming a number of competitive baselines. Moreover, ATM is able to extract meaningful events from news articles.