# Adversarial Feature Matching for Text Generation

arXiv: https://arxiv.org/abs/1706.03850 · HTML: https://mlsanity.com/paper/1706.03850/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2017.

## Built on by (most cited first, 30 of 58)

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- [Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game](https://mlsanity.com/paper/2311.08045/) (2023)
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- [DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation](https://mlsanity.com/paper/2404.00264/) (2024)
- [The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms](https://mlsanity.com/paper/2203.06369/) (2022)
- [A survey on text generation using generative adversarial networks](https://mlsanity.com/paper/2212.11119/) (2022)
- [TextBox: A Unified, Modularized, and Extensible Framework for Text Generation](https://mlsanity.com/paper/2101.02046/) (2021)
- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers](https://mlsanity.com/paper/2307.12667/) (2023)
- [An improved tabular data generator with VAE-GMM integration](https://mlsanity.com/paper/2404.08434/) (2024)
- [Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators](https://mlsanity.com/paper/2204.03243/) (2022)
- [Time-series Generation by Contrastive Imitation](https://mlsanity.com/paper/2311.01388/) (2023)
- [Generative Video Semantic Communication via Multimodal Semantic Fusion with Large Model](https://mlsanity.com/paper/2502.13838/) (2025)
- [A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models](https://mlsanity.com/paper/2404.14445/) (2024)
- [Better LLM Reasoning via Dual-Play](https://mlsanity.com/paper/2511.11881/) (2025)
- [Say What? Collaborative Pop Lyric Generation Using Multitask Transfer Learning](https://mlsanity.com/paper/2111.07592/) (2021)
- [Improved Training of Mixture-of-Experts Language GANs](https://mlsanity.com/paper/2302.11875/) (2023)
- [Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems](https://mlsanity.com/paper/2309.15995/) (2023)
- [Soft-Di[M]O: Improving One-Step Discrete Image Generation with Soft Embeddings](https://mlsanity.com/paper/2509.22925/) (2025)
- [Generating Symbolic Reasoning Problems with Transformer GANs](https://mlsanity.com/paper/2110.10054/) (2021)
- [A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)](https://mlsanity.com/paper/2302.10274/) (2023)
- [Deep Reinforcement Learning for Modelling Protein Complexes](https://mlsanity.com/paper/2405.02299/) (2024)
- [Conditional Loss and Deep Euler Scheme for Time Series Generation](https://mlsanity.com/paper/2102.05313/) (2021)
- [Categorical EHR Imputation with Generative Adversarial Nets](https://mlsanity.com/paper/2108.01701/) (2021)
- [Regularizing Transformers With Deep Probabilistic Layers](https://mlsanity.com/paper/2108.10764/) (2021)
- [Nested-Wasserstein Self-Imitation Learning for Sequence Generation](https://mlsanity.com/paper/2001.06944/) (2020)

## Abstract (arXiv)

The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We employ a long short-term memory network as generator, and a convolutional network as discriminator. Instead of using the standard objective of GAN, we propose matching the high-dimensional latent feature distributions of real and synthetic sentences, via a kernelized discrepancy metric. This eases adversarial training by alleviating the mode-collapsing problem. Our experiments show superior performance in quantitative evaluation, and demonstrate that our model can generate realistic-looking sentences.