The official implementation for Findings of the EMNLP 2023 paper An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations.
- Python 3.7.11
- PyTorch 1.8.0
- Transformers 4.1.1
- CUDA 11.1
Download datasets and save them in ./data.
Download knowledge and save them in ./.
You can train the models with the following codes:
--TP
: Using auxiliary label knowledge: topic--SC
: Using auxiliary label knowledge: sarcasm--MP
: Using auxiliary label knowledge: metaphor--EC
: Using auxiliary utterance knowledge: emotional cause--CS
: Using auxiliary utterance knowledge: commonsense knowledge--ACS
: Using auxiliary utterance knowledge: affective commonsense knowledge--CR
: Using auxiliary contextual knowledge: co-reference--CT
: Using auxiliary contextual knowledge: context--EC2
: Using auxiliary contextual knowledge: emotional cause
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{tu2023empirical,
title={An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations},
author={Tu, Geng and Liang, Bin and Qin, Bing and Wong, Kam-Fai and Xu, Ruifeng},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
pages={12160--12173},
year={2023}
}
The code of this repository partly relies on DAG-ERC and I would like to show my sincere gratitude to the authors behind these contributions.