Task-Oriented Dialog Generation with Enhanced Entity Representation

This is the PyTorch implementation of the paper: Task-Oriented Dialog Generation with Enhanced Entity Representation. INTERSPEECH 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{He2020,
  author={Zhenhao He and Jiachun Wang and Jian Chen},
  title={{Task-Oriented Dialog Generation with Enhanced Entity Representation}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={3905--3909},
  doi={10.21437/Interspeech.2020-1037},
  url={http://dx.doi.org/10.21437/Interspeech.2020-1037}
}

Preprocessing the datasets

To preprocess the dataset, you can run the following command:

❱❱❱ cd data/[KVR|CamRest]
❱❱❱ bash gen_data.sh

Train a model for task-oriented dialog datasets

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

❱❱❱ python myTrain.py  -ds kvr -b 8 -hdd 128 -lr 0.001 -dr 0.4

or CamRest:

❱❱❱ python myTrain.py  -ds cam -b 8 -hdd 128 -lr 0.001 -dr 0.5 --shareDec

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> 

OOV Test

If you want to train model under the OOV Test (please refer to our paper for details), you can add -ot to the function call.

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!