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

• PyTorch 0.4.0
• Python 3.6
• Numpy
• NLTK

pip install -r requirements.txt

• Use pre-trained Word2vec Download Glove word embeddings glove.twitter.27B.200d.txt from https://nlp.stanford.edu/projects/glove/ 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
  

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

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.

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}
}

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