DialogWAE

This repository is for CS6101, we are investigating this paper: DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder. The original repository is https://github.com/guxd/DialogWAE.

Poster

Dependency

• PyTorch 0.4.0
• Python 3.6
• NLTK

pip install -r requirements.txt

Train

• Use pre-trained Word2vec Download Glove word embeddings glove.twitter.27B.200d.txt from https://nlp.stanford.edu/projects/glove/ and save it to the ./data folder. The default setting use 200 dimension word embedding trained on Twitter.

• Modify the arguments at the top of train.py

• Train model by

    python train.py --visual
  

The logs and temporary results will be printed to stdout and saved in the ./output path.

• Visualize the training status in Tensorboard

    tensorboard --logdir output
  

Evaluation

Modify the arguments at the bottom of sample.py

Run model testing by:

    python sample.py

The outputs will be printed to stdout and generated responses will be saved at results.txt in the ./output path.

TF-IDF

First change models/dialogwae_gmp.py

self.fix_prior_net = MixVariationFixCompo(self.prior_net.input_size, self.prior_net.z_size, self.prior_net.n_components, self.prior_net.gumbel_temp, select_compo=[component number])

then run

   python sample.py --selected_compo [0|1|2|3|4]

After that, visualize TF-IDF by running TFIDF.ipynb

References

@inproceedings{gu2018dialogwae,
      title={Dialog{WAE}: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder},
      author={Gu, Xiaodong and Cho, Kyunghyun and Ha, Jung-Woo and Kim, Sunghun},
      journal={arXiv preprint arXiv:1805.12352},
      year={2018}
}