This repository is the implementation of our COLING 2020 Paper EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation.
- Check the packages needed or simply run the command:
pip install -r requirements.txt- The dataset (empathetic-dialogue) is preprocessed and stored in pickle format:
.
└── empathetic-dialogue
└── empdg_dataset_preproc.p
-
Download GloVe vectors from here (glove.6B.300d.txt) and put it into
/vectors/. -
For reproducibility purposes, we place the model checkpoints at Google Drive. You could download and move it under
/result/. -
To skip training, please check folder
/Predictions/.
EmpDG
# 1. Pre-train Empathetic Generator (EmpDG_woD)
# 2. Pre-train two Interactive Discriminators
# 3. Joint-train two components
python adver_train.py --cuda --label_smoothing --noam --emb_dim 300 --rnn_hidden_dim 300 --hidden_dim 300 --hop 1 --heads 2 --emotion_disc --pretrain_emb --model EmpDG --device_id 0 --save_path save/EmpDG/ --d_steps 1 --g_steps 5 --pointer_genEmpDG_woD
# Empathetic Generator
python train.py --cuda --label_smoothing --noam --emb_dim 300 --hidden_dim 300 --hop 1 --heads 2 --pretrain_emb --model EmpDG_woD --device_id 0 --save_path save/EmpDG_woD/ --pointer_genEmpDG_woG
# We regard the baseline EmoPrepend as generator, which only considers the coarse-grained emotional factor.
# We only use the semantic discriminator to distinguish the generated responses and the gold ones.
python adver_train_no_eg.py --cuda --label_smoothing --noam --emb_dim 300 --rnn_hidden_dim 300 --hidden_dim 300 --hop 1 --heads 2 --cuda --pretrain_emb --model EmpDG_woG --device_id 0 --save_path save/EmpDG_woG/ --d_steps 1 --g_steps 5 --pointer_gen Add
--resume_g --resume_dinto the command to directly use the pre-trained generator or discriminator.
EmpDG
Make sure that the trained model
result/EmpDG_best.tarexists.
python train.py --test --cuda --label_smoothing --noam --emb_dim 300 --rnn_hidden_dim 300 --hidden_dim 300 --hop 1 --heads 2 --pretrain_emb --model EmpDG --device_id 0 --save_path save/EmpDG/ --pointer_genIf you find our work useful, please cite our paper as follows:
@inproceedings{li-etal-2020-empdg,
title={EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation},
author={Qintong Li and Hongshen Chen and Zhaochun Ren and Zhaopeng Tu and Zhumin Chen},
booktitle={COLING},
year={2020},
}
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