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
python3 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.
python3 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.
python3 train.py --test --model EmpDG --cuda --label_smoothing --noam --emb_dim 300 --rnn_hidden_dim 300 --hidden_dim 300 --hop 1 --heads 2 --pretrain_emb --device_id 0 --save_path save/EmpDG/ --pointer_genpython3 interact.py --model EmpDG --cuda --label_smoothing --noam --emb_dim 300 --rnn_hidden_dim 300 --hidden_dim 300 --hop 1 --heads 2 --pretrain_emb --device_id 0 --save_path save/EmpDG/ --pointer_gen- Generation examples from an interactive environment:
| Role | Response |
|---|---|
| User | i am so excited because i am finally going to visit my old friend ! |
| EmpDG | that is awesome ! what kind of visit ? |
| User | we will enjoy chatting and playing . |
| EmpDG | that is a lot of nostalgia . |
| Role | Response |
|---|---|
| User | i argued with my brother . |
| EmpDG | oh no ! what happened ? |
| User | i don't want to recall . |
| EmpDG | i am sorry , that is not a good thing . |
If you find our work useful, please cite our paper as follows:
@inproceedings{li2020empdg,
title={EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation},
author={Li, Qintong and Chen, Hongshen and Ren, Zhaochun and Ren, Pengjie and Tu, Zhaopeng and Chen, Zhumin},
booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
pages={4454--4466},
year={2020}
}