TO-DO:

For ALL

• reuse self.reset_epoch
• ignore warning and logging of tensorflow
• gitignore
• repaire conflict of tf.app.flags and getopt
• move path define to Gan class
• specify GPU when training
• save log in experiment fold
• use json file save load hyperparameters
• new metric : number of tokens
• analyze sentence corpus
• add origin leakgan

For LeakGAN

• only bleu3 Leakgan real evaluate metric
• moidfy evaluate policy for Leakgan
• add epoch, in log head
• readable log in CLI for real train
• modify leakgan hyperparameter to origin leakgan
• save pretrain model to experiment path
• save generate file to experiment path
• create config.json

For My leakgan

• create class for myleakgan
• dis data loader to split sentence
• create config.json

Testgan

• Add Testgan
• create config file for TestGan

Introduction

Texygen is a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the diversity, the quality and the consistency of the generated texts. The Texygen platform could help standardize the research on text generation and facilitate the sharing of fine-tuned open-source implementations among researchers for their work. As a consequence, this would help in improving the reproductivity and reliability of future research work in text generation.

For more details, please refer to our SIGIR 2018 paper: Texygen: A Benchmarking Platform for Text Generation Models by Yaoming Zhu et al.

Should you have any questions and enquiries, please feel free to contact Yaoming Zhu (ym-zhu [AT] outlook.com) and Weinan Zhang (wnzhang [AT] sjtu.edu.cn).

Requirement

We suggest you run the platform under Python 3.6+ with following libs:

• TensorFlow >= 1.5.0
• Numpy 1.12.1
• Scipy 0.19.0
• NLTK 3.2.3
• CUDA 7.5+ (Suggested for GPU speed up, not compulsory)

Or just type pip install -r requirements.txt in your terminal.

Implemented Models and Original Papers

• SeqGAN - SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

• MaliGAN - Maximum-Likelihood Augmented Discrete Generative Adversarial Networks

• RankGAN - Adversarial ranking for language generation

• LeakGAN - Long Text Generation via Adversarial Training with Leaked Information

• TextGAN - Adversarial Feature Matching for Text Generation

• GSGAN - GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution

Get Started

git clone https://github.com/geek-ai/Texygen.git
cd Texygen
# run SeqGAN with default setting
python3 main.py

More detailed documentation for the platform and code setup is provided here.

Evaluation Results

BLEU on image COCO caption test dataset:

	SeqGAN	MaliGAN	RankGAN	LeakGAN	TextGAN	MLE
BLEU2	0.745	0.673	0.743	0.746	0.593	0.731
BLEU3	0.498	0.432	0.467	0.528	0.463	0.497
BLEU4	0.294	0.257	0.264	0.355	0.277	0.305
BLEU5	0.180	0.159	0.156	0.230	0.207	0.189

Mode Collapse (Self-BLEU):

	SeqGAN	MaliGAN	RankGAN	LeakGAN	TextGAN	MLE
S-BLEU2	0.950	0.918	0.959	0.966	0.942	0.916
S-BLEU3	0.840	0.781	0.882	0.913	0.931	0.769
S-BLEU4	0.670	0.606	0.762	0.848	0.804	0.583
S-BLEU5	0.489	0.437	0.618	0.780	0.746	0.408

More detailed benchmark settings and evaluation results are provided here.

Reference

@article{zhu2018texygen,
  title={Texygen: A Benchmarking Platform for Text Generation Models},
  author={Zhu, Yaoming and Lu, Sidi and Zheng, Lei and Guo, Jiaxian and Zhang, Weinan and Wang, Jun and Yu, Yong},
  journal={SIGIR},
  year={2018}
}