DialogWAE

It is a PyTorch implementation of the DialogWAE model described in DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder.

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.

References

If you use any source code included in this toolkit in your work, please cite the following paper:

@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}
}

LICENSE

Copyright 2018 NAVER Corp. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America and IDIAP Research Institute nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

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