Sequicity
Source code for the ACL 2018 paper entitled "Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures" by Wenqiang Lei et al.
@inproceedings{lei2018sequicity,
title={Sequicity: Simplifying task-oriented dialogue systems with single sequence-to-sequence architectures},
author={Lei, Wenqiang and Jin, Xisen and Kan, Min-Yen and Ren, Zhaochun and He, Xiangnan and Yin, Dawei},
booktitle={ACL},
year={2018}
}
Training with default parameters
python model.py -mode train -model [tsdf-camrest|tsdf-kvret]
(optional: configuring hyperparameters with cmdline)
python model.py -mode train -model [tsdf-camrest|tsdf-kvret] -cfg lr=0.003 batch_size=32
Testing
python model.py -mode test -model [tsdf-camrest|tsdf-kvret]
Reinforcement fine-tuning
python model.py -mode rl -model [tsdf-camrest|tsdf-kvret] -cfg lr=0.0001
Before running
- Install required python packages. We used pytorch 0.3.0 and python 3.6 under Linux operating system.
pip install -r requirements.txt
- Make directories under PROJECT_ROOT.
mkdir vocab
mkdir log
mkdir results
mkdir models
mkdir sheets
- Download pretrained Glove word vectors and place them in PROJECT_ROOT/data/glove.