The official implementation for the conference of the ECAI 2023 paper Do Topic and Causal Consistency Affect Emotion Cognition? A Graph Interactive Network for Conversational Emotion Detection.

• Python 3.7.11
• PyTorch 1.8.0
• Transformers 4.1.1
• CUDA 11.1

Download datasets and save them in ./data.

Download topics and save them in ./data.

You can train the models with the following codes:

For IEMOCAP: python run.py --dataset IEMOCAP --gnn_layers 4 --lr 0.0005 --batch_size 16 --epochs 30 --dropout 0.2

For MELD: python run.py --dataset MELD --lr 0.00001 --batch_size 64 --epochs 70 --dropout 0.1

For EmoryNLP: python run.py --dataset EmoryNLP --lr 0.00005 --batch_size 32 --epochs 100 --dropout 0.3

If you find our work useful for your research, please kindly cite our paper as follows:

@inproceedings{tu2023topic,
  title={Do topic and causal consistency affect emotion cognition? a graph interactive network for conversational emotion detection},
  author={Tu, Geng and Liang, Bin and Lyu, Xiucheng and Gui, Lin and Xu, Ruifeng},
  booktitle={In The 26th European Conference on Artificial Intelligence (ECAI’23)},
  pages={2362--2369},
  year={2023}
}

The code of this repository partly relies on WTM and DAG-ERC. I would like to show my sincere gratitude to the authors behind these contributions.