We provide a TensorFlow implementation of the CVAE-based dialog model described in Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders, accepted as a long paper in ACL 2017. See the paper for more details.
- TensorFlow 0.12.1+
- Python 2.7
- Numpy
- NLTK
- You may need to pip install beeprint if the module is missing
python kgcvae_swda.py
will run default training and save model to ./working
Modify the TF flags at the top of kgcvae_swda.py as follows to run a existing model
forward_only: False -> True
test_path: set to the folder contains the model. E.g. runxxxx
Then you can run the model by:
python kgcvae_swda.py
The outputs will be printed to stdout and generated responses will be saved at test.txt in the test_path.
Download Glove word embeddings from https://nlp.stanford.edu/projects/glove/ The default setting use 200 dimension word embedding trained on Twitter.
At last, set word2vec_path at line 15 of kgcvae_swda.py.
If you want to train the model on your own data. Please create a pickle file has the following format:
# The top directory is a python dictionary
type(data) = dict
data.keys() = ['train', 'valid', 'test']
# Train/valid/test is a list, each element is one dialog
train = data['train']
type(train) = list
# Each dialog is a dict
dialog = train[0]
type(dialog)= dict
dialog.keys() = ['A', 'B', 'topic', 'utts']
# A, B contain meta info about speaker A and B.
# topic defines the dialog prompt topic in Switchboard Corpus.
# utts is a list, each element is a tuple that contain info about an utterance
utts = dialog['utts']
type(utts) = list
utts[0] = ("A" or "B", "utterance in string", [dialog_act, other_meta_info])
# For example, a utterance look like this:
('B','especially your foreign cars',['statement-non-opinion'])
Put the resulting file into ./data and set the data_dir in kgcvae_swda.py
If you use any source codes or datasets included in this toolkit in your work, please cite the following paper. The bibtex are listed below:
[Zhao et al, 2017]:
@article{zhao2017learning,
title={Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders},
author={Zhao, Tiancheng and Zhao, Ran and Eskenazi, Maxine},
journal={arXiv preprint arXiv:1703.10960},
year={2017}
}