# Adversarial Generation of Natural Language

arXiv: https://arxiv.org/abs/1705.10929 · HTML: https://mlsanity.com/paper/1705.10929/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2017.

## Built on by (most cited first, 30 of 33)

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- [Language Models are Realistic Tabular Data Generators](https://mlsanity.com/paper/2210.06280/) (2022)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models](https://mlsanity.com/paper/2305.18455/) (2023)
- [LeanAgent: Lifelong Learning for Formal Theorem Proving](https://mlsanity.com/paper/2410.06209/) (2024)
- [SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses](https://mlsanity.com/paper/2404.04298/) (2024)
- [Complexity Matters: Rethinking the Latent Space for Generative Modeling](https://mlsanity.com/paper/2307.08283/) (2023)
- [GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Network](https://mlsanity.com/paper/2108.03857/) (2021)
- [Feedback Friction: LLMs Struggle to Fully Incorporate External Feedback](https://mlsanity.com/paper/2506.11930/) (2025)
- [Time-series Transformer Generative Adversarial Networks](https://mlsanity.com/paper/2205.11164/) (2022)
- [Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models](https://mlsanity.com/paper/2210.12607/) (2022)
- [Bi-fidelity Variational Auto-encoder for Uncertainty Quantification](https://mlsanity.com/paper/2305.16530/) (2023)
- [Synthetic data generation for a longitudinal cohort study -- Evaluation, method extension and reproduction of published data analysis results](https://mlsanity.com/paper/2305.07685/) (2023)
- [Categorical EHR Imputation with Generative Adversarial Nets](https://mlsanity.com/paper/2108.01701/) (2021)
- [Sometimes We Want Translationese](https://mlsanity.com/paper/2104.07623/) (2021)
- [SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network](https://mlsanity.com/paper/2107.01330/) (2021)
- [Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2207.03332/) (2022)
- [GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation](https://mlsanity.com/paper/2501.02598/) (2025)
- [Adversarial Machine Learning in Text Analysis and Generation](https://mlsanity.com/paper/2101.08675/) (2021)
- [Translating synthetic natural language to database queries: a polyglot deep learning framework](https://mlsanity.com/paper/2104.07010/) (2021)
- [Topic-to-Essay Generation with Comprehensive Knowledge Enhancement](https://mlsanity.com/paper/2106.15142/) (2021)
- [Survey of Generative Methods for Social Media Analysis](https://mlsanity.com/paper/2112.07041/) (2021)
- [Why GANs are overkill for NLP](https://mlsanity.com/paper/2205.09838/) (2022)
- [Evolutionary Curriculum Training for DRL-Based Navigation Systems](https://mlsanity.com/paper/2306.08870/) (2023)
- [SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization](https://mlsanity.com/paper/2403.13240/) (2024)
- [Noun2Verb: Probabilistic frame semantics for word class conversion](https://mlsanity.com/paper/2205.06321/) (2022)
- [A big data approach towards sarcasm detection in Russian](https://mlsanity.com/paper/2306.00445/) (2023)
- [Empowering NLG: Offline Reinforcement Learning for Informal Summarization in Online Domains](https://mlsanity.com/paper/2306.17174/) (2023)
- [Adversarial Conversational Shaping for Intelligent Agents](https://mlsanity.com/paper/2307.11785/) (2023)
- [Feature Extraction Using Deep Generative Models for Bangla Text Classification on a New Comprehensive Dataset](https://mlsanity.com/paper/2308.13545/) (2023)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.