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Adversarial Generation of Natural Language

arXiv 1705.10929 ↗First posted 31 May 2017cs.CL cs.AI cs.NEcited by 33

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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.

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

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Citing papers per year: 2021: 10; 2022: 6; 2023: 10; 2024: 4; 2025: 3.

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  1. 2021Deep Neural Networks and Tabular Data: A Surveycited by 491
  2. 2022Language Models are Realistic Tabular Data Generatorscited by 294
  3. 2021Curriculum Learning: A Surveycited by 259
  4. 2023Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Modelscited by 236
  5. 2024LeanAgent: Lifelong Learning for Formal Theorem Provingcited by 35
  6. 2024SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responsescited by 26
  7. 2023Complexity Matters: Rethinking the Latent Space for Generative Modelingcited by 19
  8. 2021GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Networkcited by 15
  9. 2025Feedback Friction: LLMs Struggle to Fully Incorporate External Feedbackcited by 11
  10. 2022Time-series Transformer Generative Adversarial Networkscited by 10
  11. 2022Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Modelscited by 10
  12. 2023Bi-fidelity Variational Auto-encoder for Uncertainty Quantificationcited by 8
21 more
  1. 2023Synthetic data generation for a longitudinal cohort study -- Evaluation, method extension and reproduction of published data analysis resultscited by 5
  2. 2021Categorical EHR Imputation with Generative Adversarial Netscited by 4
  3. 2021Sometimes We Want Translationesecited by 3
  4. 2021SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Networkcited by 3
  5. 2022Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networkscited by 3
  6. 2025GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generationcited by 3
  7. 2021Adversarial Machine Learning in Text Analysis and Generationcited by 2
  8. 2021Translating synthetic natural language to database queries: a polyglot deep learning frameworkcited by 2
  9. 2021Topic-to-Essay Generation with Comprehensive Knowledge Enhancementcited by 2
  10. 2021Survey of Generative Methods for Social Media Analysiscited by 1
  11. 2022Why GANs are overkill for NLPcited by 1
  12. 2023Evolutionary Curriculum Training for DRL-Based Navigation Systemscited by 1
  13. 2024SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarizationcited by 1
  14. 2022Noun2Verb: Probabilistic frame semantics for word class conversioncited by 0
  15. 2023A big data approach towards sarcasm detection in Russiancited by 0
  16. 2023Empowering NLG: Offline Reinforcement Learning for Informal Summarization in Online Domainscited by 0
  17. 2023Adversarial Conversational Shaping for Intelligent Agentscited by 0
  18. 2023Feature Extraction Using Deep Generative Models for Bangla Text Classification on a New Comprehensive Datasetcited by 0
  19. 2023Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birdscited by 0
  20. 2024Synthetic Test Data Generation Using Recurrent Neural Networks: A Position Papercited by 0
  21. 2025GRADE: Replacing Policy Gradients with Backpropagation for LLM Alignmentcited by 0