The code for the paper RFM: response-aware feedback mechanism for background based conversation.
If you use any source code included in this repo in your work, please cite the following paper.
@article{chen2022rfm,
title={RFM: response-aware feedback mechanism for background based conversation},
author={Chen, Jiatao and Zeng, Biqing and Du, Zhibin and Deng, Huimin and Xu, Mayi and Gan, Zibang and Ding, Meirong},
journal={Applied Intelligence},
year={2022},
publisher={Springer},
doi={10.1007/s10489-022-04056-4}
}
- python 3.7
- pytorch 1.7.0
- Download the raw data version of Holl-E, and put the raw data files (train_data.json, dev_data.json and test_data.json) in the directory
dataset/holl/raw_data/
. - Then, run the preprocessing script:
python Prepare_holl.py
- As for Wizard of Wikipedia(WoW) dataset, we use the DukeNet version. Download the Wizard of Wikipedia dataset, and put data files in the directory
dataset/wizard_of_wikipedia/
. - Download the
glove.6B.300d.txt
and put it indataset/holl_e/oracle/
,dataset/holl_e/mixed/
anddataset/wizard_of_wikipedia/
.
To train or test your model, run:
# Holl-E dataset
python -m torch.distributed.launch --nproc_per_node=num_GPU Run_RFM_Holl.py --mode='train/test'
# Wizard of Wikipedia dataset
python -m torch.distributed.launch --nproc_per_node=num_GPU Run_RFM_WoW.py --mode='train/test'
If you want to run multiple references(MR) test version in Holl-E dataset, please add --test='MR'
in the run script.
We upload our model checkpoints on two datasets, you can manually download them at here.