NOTE:

This is a zip copy of the project located at url https://github.com/geek-ai/Texygen this work is not my own, but is rather the basis for related works.

<---Original README.md below --->

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

Newly Added Models

CGan - Conditional Generative Adversarial Model
INFOGAN -
LSGAN -

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}
}

eordanis / CIS-700