Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog

This repository contains the PyTorch implementation of the paper:

Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog. Libo Qin, Xiao Xu, Wanxiang Che, Yue Zhang, Ting Liu. ACL 2020. [PDF(Arxiv)]

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

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In the following, we will guide you how to use this repository step by step.

Architecture

Results

Preparation

Our code is based on PyTorch 1.2. Required python packages:

numpy==1.18.1
tqdm==4.32.1
torch==1.2.0

We highly suggest you using Anaconda to manage your python environment.

How to Run it

The script myTrain.py acts as a main function to the project, you can run the experiments by the following commands.

# SMD dataset
python myTrain.py -gpu=True -ds=kvr -dr=0.2 -bsz=16 -an=SMD -op=SMD.log
# MultiWOZ 2.1 dataset
python myTrain.py -gpu=True -ds=woz -dr=0.1 -bsz=32 -an=WOZ -op=WOZ.log

We also provide our reported model parameters in the save/best directory, you can run the following command to evaluate them and so on.

python myTrain.py -gpu=True -e=0 -ds=kvr -bsz=16 -path=save/best/SMD -op=SMD.log
python myTrain.py -gpu=True -e=0 -ds=woz -bsz=32 -path=save/best/MultiWOZ -op=WOZ.log

Due to some stochastic factors(e.g., GPU and environment), it maybe need to slightly tune the hyper-parameters using grid search to reproduce the results reported in our paper. All the hyper-parameters are in the utils/config.py and here are the suggested hyper-parameter settings:

Dropout ratio [0.1, 0.15, 0.2, 0.25, 0.3]
Batch size [16, 32]
Teacher forcing ratio [0.7, 0.8, 0.9, 1.0]

If you have any question, please issue the project or email me and we will reply you soon.

Acknowledgement

Global-to-local Memory Pointer Networks for Task-Oriented Dialogue. Chien-Sheng Wu, Richard Socher, Caiming Xiong. ICLR 2019. [PDF] [Open Reivew] [Code]