@inproceedings{wang-etal-2025-ecc,
    title = "{ECC}: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation",
    author = "Wang, Xu  and
      Wang, Bo  and
      Tang, Yihong  and
      Zhao, Dongming  and
      Liu, Jing  and
      He, Ruifang  and
      Hou, Yuexian",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.367/",
    pages = "5475--5485",
    abstract = "Empathy improves human-machine dialogue systems by enhancing the user{'}s experience. While traditional models have aimed to detect and express users' emotions from dialogue history, they neglect the crucial and complex interactions among emotion, emotion causes, and commonsense. To address this, we introduce the ECC (Emotion, Cause, and Commonsense) framework, which leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. ECC further employs novel loss functions to refine the interplay of three factors and generates empathetic responses using an energy-based model supported by ODE sampling. Empirical results on the EmpatheticDialogues dataset demonstrate that ECC outperforms existing baselines, offering a robust solution for empathetic dialogue generation."
}