The code for ACL2023 paper: 《DualGATs: Dual Graph Attention Networks for Emotion Recognition in Conversations》

Requirements

• Python 3.6.13
• PyTorch 1.7.1+cu110

With Anaconda, we can create the environment with the provided environment.yml:

conda env create --file environment.yml 
conda activate MMERC

The code has been tested on Ubuntu 16.04 using a single GPU.

Run Steps

1. Please download the four ERC datasets (including pre-processed discourse graphs and RoBERTa utterance feature) and put them in the data folder. Here we utilize the data and codes from here to pre-train a conversation discourse parser and use that parser to extract discourse graphs in the four ERC datasets. And we utilize the codes from here to extract utterance feature.
2. Run our model:

# For IEMOCAP:
CUDA_VISIBLE_DEVICES=0 python main.py --dataset IEMOCAP --lr 1e-4 --dropout 0.2 --batch_size 16 --gnn_layers 2
# For MELD:
CUDA_VISIBLE_DEVICES=0 python main.py --dataset MELD --lr 1e-4 --dropout 0.3 --batch_size 32 --gnn_layers 2
# For EmoryNLP:
CUDA_VISIBLE_DEVICES=0 python main.py --dataset EmoryNLP --lr 1e-4 --dropout 0.1 --batch_size 32 --gnn_layers 2
# For DailyDialog:
CUDA_VISIBLE_DEVICES=0 python main.py --dataset DailyDialog --lr 5e-5 --dropout 0.4 --batch_size 64 --gnn_layers 3

Citation

@inproceedings{zhang2023dualgats,
  title={DualGATs: Dual Graph Attention Networks for Emotion Recognition in Conversations},
  author={Zhang, Duzhen and Chen, Feilong and Chen, Xiuyi},
  booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={7395--7408},
  year={2023}
}