OptiGAN

This repository contains the code for text generation results of the paper:
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation, The 2020 International Joint Conference on Neural Networks (IJCNN), 2020.

Dependencies

This project uses Python 3.6.x, with the following lib dependencies:

• Tensorflow 1.4
• Numpy 1.14.1
• Matplotlib 2.2.0
• Scipy 1.0.0
• NLTK 3.2.3
• tqdm 4.19.6

Instructions

The experiments folders contain scripts for starting the different experiments. For example, to reproduce the COCO Image Captions experiments, you can try :

cd real/experiments
python coco_lstmgan_pg_baseline_mle_gan.py [job_id] [gpu_id]

or EMNLP2017 WMT News:

cd real/experiments
python3 emnlp_small_lstmgan_pg_baseline_mle_gan.py [job_id] [gpu_id]

Note to replace [job_id] and [gpu_id] with appropriate numerical values, (0, 0) for example.

Reference

To cite this work, please use:

@INPROCEEDINGS{9206842,
  author={M. {Hossam} and T. {Le} and V. {Huynh} and M. {Papasimeon} and D. {Phung}},
  booktitle={2020 International Joint Conference on Neural Networks (IJCNN)}, 
  title={{OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation}}, 
  year={2020},
  volume={},
  number={},
  pages={1-8}
}

Acknowledgement

This code is based on RELGAN and the previous benchmarking platform Texygen.