# A survey on text generation using generative adversarial networks

arXiv: https://arxiv.org/abs/2212.11119 · HTML: https://mlsanity.com/paper/2212.11119/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 68 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [Bridging the Gap Between Value and Policy Based Reinforcement Learning](https://mlsanity.com/paper/1702.08892/) (2017)
- [Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models](https://mlsanity.com/paper/1705.10843/) (2017)
- [Long Text Generation via Adversarial Training with Leaked Information](https://mlsanity.com/paper/1709.08624/) (2017)
- [MaskGAN: Better Text Generation via Filling in the______](https://mlsanity.com/paper/1801.07736/) (2018)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)
- [Adversarial Feature Matching for Text Generation](https://mlsanity.com/paper/1706.03850/) (2017)
- [Adversarial Ranking for Language Generation](https://mlsanity.com/paper/1705.11001/) (2017)
- [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution](https://mlsanity.com/paper/1611.04051/) (2016)
- [Toward Diverse Text Generation with Inverse Reinforcement Learning](https://mlsanity.com/paper/1804.11258/) (2018)
- [DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text](https://mlsanity.com/paper/1802.01345/) (2018)
- [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks](https://mlsanity.com/paper/1702.07983/) (2017)
- [TextGAIL: Generative Adversarial Imitation Learning for Text Generation](https://mlsanity.com/paper/2004.13796/) (2020)
- [Boundary-Seeking Generative Adversarial Networks](https://mlsanity.com/paper/1702.08431/) (2017)
- [Adversarial Text Generation via Feature-Mover's Distance](https://mlsanity.com/paper/1809.06297/) (2018)
- [Language Generation with Recurrent Generative Adversarial Networks without Pre-training](https://mlsanity.com/paper/1706.01399/) (2017)
- [Self-Adversarial Learning with Comparative Discrimination for Text Generation](https://mlsanity.com/paper/2001.11691/) (2020)
- [Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation](https://mlsanity.com/paper/2003.11530/) (2020)
- [BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation](https://mlsanity.com/paper/1806.08097/) (2018)
- [Adversarial Text Generation Without Reinforcement Learning](https://mlsanity.com/paper/1810.06640/) (2018)
- [Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation](https://mlsanity.com/paper/1904.07293/) (2019)
- [Text Generation Based on Generative Adversarial Nets with Latent Variable](https://mlsanity.com/paper/1712.00170/) (2017)

## Built on by (most cited first, 16 of 16)

- [SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning](https://mlsanity.com/paper/2509.09674/) (2025)
- [RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model](https://mlsanity.com/paper/2309.00810/) (2023)
- [PAC-Bayesian Generalization Bounds for Adversarial Generative Models](https://mlsanity.com/paper/2302.08942/) (2023)
- [Reinforcement Learning for Generative AI: A Survey](https://mlsanity.com/paper/2308.14328/) (2023)
- [Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding](https://mlsanity.com/paper/2402.19009/) (2024)
- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [Gaussian Harmony: Attaining Fairness in Diffusion-based Face Generation Models](https://mlsanity.com/paper/2312.14976/) (2023)
- [A data science and machine learning approach to continuous analysis of Shakespeare's plays](https://mlsanity.com/paper/2301.06024/) (2023)
- [PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer](https://mlsanity.com/paper/2404.04886/) (2024)
- [Resource Governance in Networked Systems via Integrated Variational Autoencoders and Reinforcement Learning](https://mlsanity.com/paper/2410.23393/) (2024)
- [Conditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data](https://mlsanity.com/paper/2407.04990/) (2024)
- [VicSim: Enhancing Victim Simulation with Emotional and Linguistic Fidelity](https://mlsanity.com/paper/2501.03139/) (2025)
- [Prime Convolutional Model: Breaking the Ground for Theoretical Explainability](https://mlsanity.com/paper/2503.02773/) (2025)
- [Structural System Identification via Validation and Adaptation](https://mlsanity.com/paper/2506.20799/) (2025)
- [Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation](https://mlsanity.com/paper/2508.18148/) (2025)
- [Generative AI and Federated Learning for Intrusion Detection Systems: A Survey](https://mlsanity.com/paper/2607.01305/) (2026)

## Abstract (arXiv)

This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called "natural" language. Nevertheless, adversarial text generation is not a simple task as its foremost architecture, the Generative Adversarial Networks, were designed to cope with continuous information (image) instead of discrete data (text). Thus, most works are based on three possible options, i.e., Gumbel-Softmax differentiation, Reinforcement Learning, and modified training objectives. All alternatives are reviewed in this survey as they present the most recent approaches for generating text using adversarial-based techniques. The selected works were taken from renowned databases, such as Science Direct, IEEEXplore, Springer, Association for Computing Machinery, and arXiv, whereas each selected work has been critically analyzed and assessed to present its objective, methodology, and experimental results.