Implementations of the models in the paper "Deconvolutional Paragraph Representation Learning" by Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao and Lawrence Carin, NIPS 2017
- CUDA, cudnn
- Tensorflow (version >1.0). We used tensorflow 1.2.
Run:
pip install -r requirements.txt
to install requirements
- Run:
python demo.py
for reconstruction task - Run:
python char_correction.py
for character-level correction task - Run:
python semi_supervised.py
for semi-supervised task - Options: options can be made by changing
option
class in the demo.py code.
opt.n_hidden
: number of hidden units.opt.layer
: number of CNN/DCNN layer [2,3,4].opt.lr
: learning rate.opt.batch_size
: number of batchsize.
- Training roughly takes 6-7 hours (around 10-20 epochs) (for recontruction task) to converge on a K80 GPU machine.
- See
output.txt
for a sample of screen output for reconstruction task.
- Download from :
- Reconstruction: Hotel review (1.52GB)
- Char-level correction: Yahoo! review (character-level, 451MB)
- Semi-supervised classification: Yelp review (629MB)
Please cite our paper if it helps with your research
- Arxiv link: https://arxiv.org/abs/1708.04729
@inproceedings{zhang2017deconvolutional,
title={Deconvolutional Paragraph Representation Learning},
author={Zhang, Yizhe and Shen, Dinghan and Wang, Guoyin and Gan, Zhe and Henao, Ricardo and Carin, Lawrence},
Booktitle={NIPS},
year={2017}
}
For any question or suggestions, feel free to contact [email protected]