Skip to content
PyTorch codebase for zero-shot dialog generation SIGDIAL 2018, It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data Init commit Jun 18, 2018
zsdg Init commit Jun 18, 2018
.gitignore
LICENSE
README.md Update README.md Oct 9, 2018
_config.yml Set theme jekyll-theme-cayman Jun 24, 2018
sigdial_zs_ed.png Init commit Jun 18, 2018
simdial-zsdg.py simdial-zsdg's args fixed Jan 8, 2019
stanford-zsdg.py stanford-zsdg's args fixed Jan 8, 2019

README.md

Zero-shot Dialog Generation (ZSDG) for End-to-end Neural Dialog Models

Codebase for Zero-Shot Dialog Generation with Cross-Domain Latent Actions, published as a long paper in SIGDIAL 2018. Reference information is in the end of this page. Presentation slides can be found here.

This work won the best paper award at SIGDIAL 2018.

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:

@article{zhao2018zero,
  title={Zero-Shot Dialog Generation with Cross-Domain Latent Actions},
  author={Zhao, Tiancheng and Eskenazi, Maxine},
  journal={arXiv preprint arXiv:1805.04803},
  year={2018}
}

Requirements

python 2.7
pytorch >= 0.3.0.post4
numpy
nltk

Datasets

The data folder contains three datasets:

Getting Started

The following scripts implement 4 different models, including:

  • Baseline: standard attentional encoder-decoder and encoder with pointer-sentinel-mixture decoder (see the paper for details).
  • Out Models: cross-domain Action Matching training for the above two baseline systems.

Training

Run the following to experiment on the SimDial dataset

python simdial-zsdg.py

Run the following to experiment on the Stanford Multi-Domain Dataset

python stanford-zsdg.py

Switching Model

The hyperparameters are exactly the same for the above two scripts. To train different models, use the following configurations. The following examples are for simdial-zsdg.py, which also apply to stanford-zsdg.py.

For baseline model with attetnion decoder:

python simdial-zsdg.py --action_match False --use_ptr False

For baseline model with pointer-sentinel mixture decoder:

python simdial-zsdg.py --action_match False --use_ptr True    

For action matching model with attetnion decoder:

python simdial-zsdg.py --action_match True --use_ptr False

For action matching model with attetnion decoder:

python simdial-zsdg.py --action_match True --use_ptr True    

Hyperparameters

The following are some of key hyperparameters:

  • action_match: if or not using the proposed AM algorithm for training
  • target_example_cnt: the number of seed response from each domain used for training.
  • use_ptr: if or not using pointer-sentinel-mixture decoder
  • black_domains: define which domains are excluded from training
  • black_ratio: the percentage of training data from black_domains are excluded. Range=[0,1], where 1 means removed 100% of the training data.
  • forward_only: use existing model or train a new one
  • load_sess: the path to the existing model
  • rnn_cell: the type of RNN cell, supporting LSTM or GRU
  • dropout: the chance for dropout.

Test a existing model

All trained models and log files are saved to the log folder. To run a existing model, you can:

  • Set the forward_only argument to be True
  • Set the load_sess argument to te the path to the model folder in log
  • Run the script
You can’t perform that action at this time.