Source code for our paper "EmoDM: Empathetic Response Generation with Emotion-aware Dialogue Management"

Model Overview

Environment Requirement

• pytorch >= 1.4
• sklearn
• nltk
• numpy
• bert-score

Dataset

you can directly use the processed dataset located in data/empathetic:

├── data
│   ├── empathetic
│   │   ├── parsed_emotion_Ekman_intent_test.json
│   │   ├── parsed_emotion_Ekman_intent_train.json
│   │   ├── parsed_emotion_Ekman_intent_valid.json
│   │   ├── emotion_intent_trans.mat
│   │   ├── goEmotion_emotion_trans.mat

Or you want to reproduce the data annotated with goEmotion emotion classifier and empathetic intent classifier, you can run the command:

• convert raw csv empathetic dialogue data into json format. (origin dataset link: EmpatheticDialogues)

  bash preprocess_raw.sh

• train emotion classfier with goEmotion dataset and annotate (origin dataset link: goEmotion). Here $BERT_DIR is your pretrained BERT model directory which includes vocab.txt, config.json and pytorch_model.bin, here we simply use bert-base-en from Hugginface

  bash ./bash/emotion_annotate.sh  $BERT_DIR 32 0.00005 16 3 1024 2 0.1

• train intent classfier with empathetic intent dataset and annotate (origin dataset link: Empathetic_Intent)

  bash ./bash/intent_annotate.sh  $BERT_DIR 32 0.00005 16 3 1024 2 0.1

• build prior emotion-emotion and emotion-intent transition matrix

  bash ./bash/build_transition_mat.sh

Train

For training the LM-based model, you need to download bert-base-en and gpt2-small from Hugginface first, then run the following command. Here $GPT_DIR and $BERT_DIR are the downloaded model directory:

bash ./bash/train_LM.sh --gpt_path $GPT_DIR --bert_path $BERT_DIR --gpu_id 2 --epoch 5 --lr_NLU 0.00003 --lr_NLG 0.00008 --bsz_NLU 16 --bsz_NLG 16

For training the Trs-based model, we use glove.6B.300d as the pretrained word embeddings. You can run the following command to train model. Here $GLOVE is the glove embedding txt file.

bash ./bash/train_Trs.sh --gpu_id 2 --epoch 15 --lr_NLU 0.00007 --lr_NLG 0.0015 --bsz_NLU 16 --bsz_NLG 16 --glove $GLOVE

Evaluate

To generate the automatic metric results, firstly you need to make sure that bert-score is successfully installed. In our paper, we use roberta-large-en rescaled with baseline to calculate BERTScore. You can download roberta-large-en from Hugginface. For the rescaled_baseline file, we can download it from here and put it under the roberta-large-en model directory.

Then you can run the following command to get the result, here $hypothesis and $reference are the generated response file and ground-truth response file. $result is the output result file. $ROBERTA_DIR is the downloaded roberta-large-en model directory.

To evaluate LM-based model, the command is:

bash ./bash/eval.sh --hyp $hypothesis --ref ./data/empathetic/ref.txt --out $result --bert $ROBERTA_DIR --gpu_id 0 --mode LM

To evaluate Trs-based model, the command is:

bash ./bash/eval.sh --hyp $hypothesis --ref ./data/empathetic/ref_tokenize.txt --out $result --bert $ROBERTA_DIR --gpu_id 0 --mode Trs