Code for our paper, "Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing", IJCAI 2023, AI and Social Good track.

• torch==1.13.1+cu117
• transformers==4.17.0
• sklearn
• fitlog

Download required data for training of the second stage (including the base models we used, the raw texts of dialogues, the features of utterances, and the sample sets):

• MELD: MELD_data
• IEMOCAP: IEMOCAP_data
• DailyDialog: DailyDialog_data
• SentiWordNet: SentiWordNet_3.0.0

Train the second stage model with the following commands:

• MEDL: bash train_prompt2.sh MELD 42 stage2 128 0.3 ./data/MELD/meld_stage1 3 bert_score
• IEMOCAP: bash train_prompt2.sh IEMOCAP 43 stage2 160 0.1 ./data/IEMOCAP/iemo_stage1 3 bert_score
• DailyDialog: bash train_prompt2.sh DailyDialog 2 stage2 128 0.2 ./data/DailyDialog/dd_stage1 5 bm25

The optim values of these hyper-parameters(alpha and k) can vary under different seeds or environments (similar to CoG-BART), and adjustments may be needed in different experimental settings.

We also provide checkpoints for reproduction:

• MELD: MELD_checkpoint 提取码：n5nn bash eval_prompt2.sh MELD 128 0.3 ./data/MELD/meld_stage2 3 bert_score
• IEMOCAP: IEMOCAP_checkpoint 提取码：zfta bash eval_prompt2.sh IEMOCAP 160 0.1 ./data/IEMOCAP/iemo_stage2 3 bert_score
• DailyDialog: DailyDialog_checkpoint 提取码：omu5 bash eval_prompt2.sh DailyDialog 128 0.2 ./data/DailyDialog/dd_stage2 5 bm25

Some code of this project are referenced from CoG-BART and DAG-ERC. We thank their open source materials for contribution to this task.