mlsanityData to 7 Oct 2026

ATM:Adversarial-neural Topic Model

arXiv 1811.00265 ↗First posted 1 Nov 2018cs.AIcited by 23

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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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Nov 2018.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 5; 2024: 4; 2025: 3; 2026: 1.

23 papers.

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  1. 2021Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherencecited by 74
  2. 2021Topic Modelling Meets Deep Neural Networks: A Surveycited by 51
  3. 2021Contrastive Learning for Neural Topic Modelcited by 48
  4. 2023Contextualized Topic Coherence Metricscited by 19
  5. 2024A Survey on Neural Topic Models: Methods, Applications, and Challengescited by 18
  6. 2024Topic Modeling as Multi-Objective Contrastive Optimizationcited by 12
  7. 2022Multilingual and Multimodal Topic Modelling with Pretrained Embeddingscited by 6
  8. 2022Manifestations of Xenophobia in AI Systemscited by 4
  9. 2023Improving Contextualized Topic Models with Negative Samplingcited by 4
  10. 2025Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluationcited by 4
  11. 2022Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per classcited by 3
  12. 2024Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classificationcited by 3
11 more
  1. 2021Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviewscited by 1
  2. 2022Neural Topic Modeling with Deep Mutual Information Estimationcited by 1
  3. 2022Towards Better Understanding with Uniformity and Explicit Regularization of Embeddings in Embedding-based Neural Topic Modelscited by 1
  4. 2023Topics in the Haystack: Extracting and Evaluating Topics beyond Coherencecited by 1
  5. 2023Dynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notescited by 1
  6. 2024Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learningcited by 1
  7. 2025Toward Purpose-oriented Topic Model Evaluation enabled by Large Language Modelscited by 1
  8. 2021Variational Gaussian Topic Model with Invertible Neural Projectionscited by 0
  9. 2023What are Public Concerns about ChatGPT? A Novel Self-Supervised Neural Topic Model Tells Youcited by 0
  10. 2025Evaluating Negative Sampling Approaches for Neural Topic Modelscited by 0
  11. 2026Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transportcited by 0