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Language Generation with Recurrent Generative Adversarial Networks without Pre-training

arXiv 1706.01399 ↗First posted 5 Jun 2017cs.CLcited by 14

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Abstract (arXiv)

Generative Adversarial Networks (GANs) have shown great promise recently in image generation. Training GANs for language generation has proven to be more difficult, because of the non-differentiable nature of generating text with recurrent neural networks. Consequently, past work has either resorted to pre-training with maximum-likelihood or used convolutional networks for generation. In this work, we show that recurrent neural networks can be trained to generate text with GANs from scratch using curriculum learning, by slowly teaching the model to generate sequences of increasing and variable length. We empirically show that our approach vastly improves the quality of generated sequences compared to a convolutional baseline.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2017.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 3; 2024: 1.

14 papers.

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  1. 2021Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networkscited by 50
  2. 2022The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithmscited by 19
  3. 2022A survey on text generation using generative adversarial networkscited by 16
  4. 2022On the Effectiveness of Pretrained Models for API Learningcited by 9
  5. 2023Continual Learning with Dirichlet Generative-based Rehearsalcited by 7
  6. 2021SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Networkcited by 3
  7. 2023Adversarial Transformer Language Models for Contextual Commonsense Inferencecited by 2
  8. 2022Why GANs are overkill for NLPcited by 1
  9. 2024ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-Image Generationcited by 1
  10. 2021Taming the Beast: Learning to Control Neural Conversational Modelscited by 0
  11. 2021OptAGAN: Entropy-based finetuning on text VAE-GANcited by 0
  12. 2021Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibrationcited by 0
2 more
  1. 2022Noun2Verb: Probabilistic frame semantics for word class conversioncited by 0
  2. 2023Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesiscited by 0