/HDNO

This is the source code for HDNO: a hierarchical model for task-oriented dialogue system.

Primary LanguagePython

HDNO

This is the codebase for ICLR 2021 paper: Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System.

If you use any source codes included in this toolkit in your work, please cite the following paper. The bibtex is listed below:

@article{wang2020modelling,
  title={Modelling hierarchical structure between dialogue policy and natural language generator with option framework for task-oriented dialogue system},
  author={Wang, Jianhong and Zhang, Yuan and Kim, Tae-Kyun and Gu, Yunjie},
  journal={arXiv preprint arXiv:2006.06814},
  year={2020}
}

Repository Structure

  • configs: hyperparameters for different experiments
  • data: dialogue dataset (MultiWowz 2.0 & MultiWoz 2.1)
  • latent_dialog: source code
  • human_evaluator.py: to evalute groud truth's performance
  • supervised.py: main entry for Supervised Learning
  • reinforce.py: main entry for Reinforcement Learning

Requirements

  1. install conda environment
conda create -n hdno python=3.6
conda activate hdno
  1. install requirements
pip install -r requirements.txt

Prepare the data

Before any operations below, please prepare your data following the script:

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

Reproduce the results

We give a script that can evaluate HDNO and show the results shown on the paper, based on the pretrained models we provide on MultiWoz 2.0 and MultiWoz 2.1:

sh reproduce.sh

Train your own models

For the convenience for freely training models, we give simple bash scripts to do it.

  1. pretraining
sh train.sh sl woz2.0 # For MultiWoz 2.0
sh train.sh sl woz2.1 # For MultiWoz 2.1
  1. hierarchical reinforcement learning (HRL)
sh train.sh rl woz2.0 # For MultiWoz 2.0
sh train.sh rl woz2.1 # For MultiWoz 2.1
  1. evaluating trained model
sh test.sh sl woz2.0 5 # For MultiWoz 2.0 pretrained model
sh test.sh sl woz2.1 5 # For MultiWoz 2.1 pretrained model
sh test.sh rl woz2.0 2 # For MultiWoz 2.0 HRL model 
sh test.sh rl woz2.1 5 # For MultiWoz 2.1 HRL model

Main results

  • The table shows the main test results of HDNO on MultiWoz 2.0 and MultiWoz 2.1 evaluated with the automatic evaluation metrics.
    result

  • The diagram demonstrates latent dialogue acts of HDNO clustered in 8 categories.
    cluster