Code for CASE

The implementation of our paper accepted by ACL 2023: CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation

Requirements

• Python==3.6.12
• torch==1.3.0+cu100
• nltk==3.4.5
• transformers==4.10.2
• vaderSentiment==3.3.2
• tensorboardX==2.5
• scikit-learn==0.24.1
• spacy==3.1.4
• numpy==1.19.5
• DownloadPretrained GloVe Embeddings 300d and save it in /vectors.

Dataset

• The preprocessed dataset is already provided at Google Driven. Change the folder name to data.
• If you want to create the dataset yourself, download the comet-atomic-2020 (BART) checkpoint and place it in /data/Comet. The preprocessed data will be automatically generated after running the main.sh script.

Training, Testing & Evaluation

bash main.sh

Citation

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

@inproceedings{DBLP:conf/acl/ZhouZW0H23,
  author       = {Jinfeng Zhou and
                  Chujie Zheng and
                  Bo Wang and
                  Zheng Zhang and
                  Minlie Huang},
  editor       = {Anna Rogers and
                  Jordan L. Boyd{-}Graber and
                  Naoaki Okazaki},
  title        = {{CASE:} Aligning Coarse-to-Fine Cognition and Affection for Empathetic
                  Response Generation},
  booktitle    = {Proceedings of the 61st Annual Meeting of the Association for Computational
                  Linguistics (Volume 1: Long Papers), {ACL} 2023, Toronto, Canada,
                  July 9-14, 2023},
  pages        = {8223--8237},
  publisher    = {Association for Computational Linguistics},
  year         = {2023},
  url          = {https://aclanthology.org/2023.acl-long.457},
  timestamp    = {Thu, 13 Jul 2023 16:47:40 +0200},
  biburl       = {https://dblp.org/rec/conf/acl/ZhouZW0H23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}