Pytorch code for EMNLP 2023 accepted-main paper "How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning" and TKDE paper "Learning a Structural Causal Model for Intuition Reasoning in Conversation" (early access)
the bibtexs are
@inproceedings{chen-etal-2023-enhance, title = "How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning", author = "Chen, Hang and Yang, Xinyu and Luo, Jing and Zhu, Wenjing", editor = "Bouamor, Houda and Pino, Juan and Bali, Kalika", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.emnlp-main.33", doi = "10.18653/v1/2023.emnlp-main.33", pages = "494--512", }
@article{chen2024learning, title={Learning a structural causal model for intuition reasoning in conversation}, author={Chen, Hang and Liao, Bingyu and Luo, Jing and Zhu, Wenjing and Yang, Xinyu}, journal={IEEE Transactions on Knowledge and Data Engineering}, year={2024}, publisher={IEEE} }
- Python
- PyTorch
- Transformers
- sklearn
- wandb
- pandas
You can download the dataset from https://drive.google.com/file/d/1GG5dYLfjTI_7907ORQJUX6q5Mg4wLI3U/view?usp=drive_link
You can train the models with the following codes:
For ERC task:
cd DAG-ECPE_ERC python run.py --args*
The details of the parameters are:
IEMOCAP: gnn_layers：1 ,batch_size：8 ,dropout：0.1,lr：0.0004,epoch：50
MELD: gnn_layers：1 ,batch_size：8 ,dropout：0.2,lr：4e-5,epoch：30
DailyDialog:gnn_layers：1 ,batch_size：16 ,dropout：0.3,lr：5e-5,epoch：40
EmoryNLP:gnn_layers：1 ,batch_size：64 ,dropout：0.1,lr：7e-4,epoch：50
For ECPE
cd DAG-ECPE python run.py --args*
The details of the parameters are: lr:6e-4
For ECSR
cd DAG-ECSR python run.py --args*
The details of parameters are: lr:6e-4