FG2SEQ: EFFECTIVELY ENCODING KNOWLEDGE FOR END-TO-END TASK-ORIENTED DIALOG

This is the PyTorch implementation of the paper: FG2SEQ: EFFECTIVELY ENCODING KNOWLEDGE FOR END-TO-END TASK-ORIENTED DIALOG. ICASSP 2020. paper_link

This code has been written using PyTorch >= 0.4. If you use any source codes included this toolkit in your work, please cite the following paper. The bibtex are listed below:

@inproceedings{he2020fg2seq,
  title={FG2SEQ: Effectively Encoding Knowledge for End-to-End Task-oriented Dialog},
  author={He, Zhenhao and He Yuhong and Wu, Qingyao and Chen Jian},
  booktitle={International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2020}
}

Preprocessing the datasets

We created preprocessing.sh to preprocess the datasets. You can run:

❱❱❱ ./preprocessing.sh

Train a model for task-oriented dialog datasets

We created myTrain.py to train models. You can run: FG2SEQ InCar:

❱❱❱ python myTrain.py -lr=0.001 -hdd=128 -dr=0.2 -bsz=16 -ds=kvr -B=10 -ss=10.0

or FG2SEQ CamRest:

❱❱❱ python myTrain.py -lr=0.001 -hdd=128 -dr=0.2 -bsz=8 -ds=cam -B=5 -ss=10.0

While training, the model with the best validation is saved. If you want to reuse a model add -path=path_name_model to the function call. The model is evaluated by using F1 and BLEU.

Test a model for task-oriented dialog datasets

We created myTest.py to test models. You can run:

❱❱❱ python myTest.py -path=<path_to_saved_model> 

Acknowledgement

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

We are highly grateful for the public code of GLMP!