Co-Gen

This is the code and data for the SIGIR'22 paper: "Structured and Natural Responses Co-generation for Conversational Search" by Chenchen Ye, Lizi Lizo, Fuli Feng, Wei Ji, and Tat-Seng Chua.

Requirements

• Python 3.6
• PyTorch 1.4.0

Other required modules are listed in requirements.txt, you could set up a conda environment for the experiment by:

conda create -n Co-Gen python=3.6
conda activate Co-Gen
pip install -r requirements.txt 

Folder

• configs: the hyperparameters for Co-Gen SL and RL training.
• data: all the needed training/validation/testing data for MultiWOZ 2.0 and MultiWOZ 2.1.
• latent_dialog: source code for Co-Gen model.
• reinforce.py: code for RL training and evaluation.
• supervised.py: code for SL training and evaluation.

Data Preparation

The input data files are zipped put under directory ./data, it should be unzipped:

unzip data/act.zip -d data
unzip data/multiwoz_2.0.zip -d data
unzip data/multiwoz_2.1.zip -d data

You can also download MultiWOZ 2.0 & 2.1 data from the official repository, and process it with its given delexicalization script.

Train a Co-Gen model

The training process contains two stage: Supervised Training and Reinforcement Learning.

SL training for MultiWOZ2.0 / 2.1:

python -u supervised.py --config_name sl_woz2.0
python -u supervised.py --config_name sl_woz2.1

RL training for MultiWOZ2.0 / 2.1:

python -u reinforce.py --config_name rl_woz2.0
python -u reinforce.py --config_name rl_woz2.1

To run evaluation on the trained SL/RL model:

python -u supervised.py --config_name sl_woz2.0 --forward_only
python -u supervised.py --config_name sl_woz2.1 --forward_only

python -u reinforce.py --config_name rl_woz2.0 --forward_only
python -u reinforce.py --config_name rl_woz2.1 --forward_only