# DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text

arXiv: https://arxiv.org/abs/1802.01345 · HTML: https://mlsanity.com/paper/1802.01345/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2018.

## Built on by (most cited first, 30 of 40)

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- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [Controllable and Diverse Text Generation in E-commerce](https://mlsanity.com/paper/2102.11497/) (2021)
- [Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training](https://mlsanity.com/paper/2105.15171/) (2021)
- [Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV](https://mlsanity.com/paper/2208.08655/) (2022)
- [Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models](https://mlsanity.com/paper/2303.12281/) (2023)
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- [DeepGen: Diverse Search Ad Generation and Real-Time Customization](https://mlsanity.com/paper/2208.03438/) (2022)
- [Stochastic Parrots Looking for Stochastic Parrots: LLMs are Easy to Fine-Tune and Hard to Detect with other LLMs](https://mlsanity.com/paper/2304.08968/) (2023)
- [Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation](https://mlsanity.com/paper/2310.12583/) (2023)
- [On Distinctive Image Captioning via Comparing and Reweighting](https://mlsanity.com/paper/2204.03938/) (2022)
- [Game of GANs: Game-Theoretical Models for Generative Adversarial Networks](https://mlsanity.com/paper/2106.06976/) (2021)
- [Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral Mapping](https://mlsanity.com/paper/2212.04002/) (2022)
- [PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism](https://mlsanity.com/paper/2212.09086/) (2022)
- [Don't Take This Out of Context! On the Need for Contextual Models and Evaluations for Stylistic Rewriting](https://mlsanity.com/paper/2305.14755/) (2023)
- [Pattern Detection in the Activation Space for Identifying Synthesized Content](https://mlsanity.com/paper/2105.12479/) (2021)
- [High-Dimensional Distribution Generation Through Deep Neural Networks](https://mlsanity.com/paper/2107.12466/) (2021)
- [A survey of diversity quantification in natural language processing: The why, what, where and how](https://mlsanity.com/paper/2507.20858/) (2025)
- [Epistemic Generative Adversarial Networks](https://mlsanity.com/paper/2603.18348/) (2026)
- [Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning](https://mlsanity.com/paper/2204.06748/) (2022)
- [A generalised novel loss function for computational fluid dynamics](https://mlsanity.com/paper/2411.17059/) (2024)
- [Adversarial Machine Learning in Text Analysis and Generation](https://mlsanity.com/paper/2101.08675/) (2021)
- [Can the Transformer Be Used as a Drop-in Replacement for RNNs in Text-Generating GANs?](https://mlsanity.com/paper/2108.12275/) (2021)
- [Stop Filtering: Multi-View Attribute-Enhanced Dialogue Learning](https://mlsanity.com/paper/2205.11206/) (2022)

## Abstract (arXiv)

Existing text generation methods tend to produce repeated and "boring" expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeatedly generated text and high reward for "novel" and fluent text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel language-model based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our model can generate substantially more diverse and informative text than existing baselines. The code is available at https://github.com/lancopku/DPGAN