The official implementation for Findings of the ACL 2023 paper Context or Knowledge is Not Always Necessary: A Contrastive Learning Framework for Emotion Recognition in Conversations.

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

Download features and save them in ./.

You can train the models with the following codes:

For IEMOCAP: python train_iemocap.py --active-listener

For DailyDialog: python train_dailydialog.py --active-listener --class-weight --residual

For MELD Emotion: python train_meld.py --active-listener --attention simple --dropout 0.5 --rec-dropout 0.3 --lr 0.0001 --mode1 2 --classify emotion --mu 0 --l2 0.00003 --epochs 60

For MELD Sentiment: python train_meld.py --active-listener --class-weight --residual --classify sentiment

For EmoryNLP Emotion: python train_emorynlp.py --active-listener --class-weight --residual

For EmoryNLP Sentiment: python train_emorynlp.py --active-listener --class-weight --residual --classify sentiment

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

@inproceedings{tu2023context,
  title={Context or knowledge is not always necessary: A contrastive learning framework for emotion recognition in conversations},
  author={Tu, Geng and Liang, Bin and Mao, Ruibin and Yang, Min and Xu, Ruifeng},
  booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
  pages={14054--14067},
  year={2023}
}

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