@inproceedings{gong-etal-2023-eliciting,
    title = "Eliciting Rich Positive Emotions in Dialogue Generation",
    author = "Gong, Ziwei  and
      Min, Qingkai  and
      Zhang, Yue",
    editor = "Chawla, Kushal  and
      Shi, Weiyan",
    booktitle = "Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.sicon-1.1",
    doi = "10.18653/v1/2023.sicon-1.1",
    pages = "1--8",
    abstract = "Positive emotion elicitation aims at evoking positive emotion states in human users in open-domain dialogue generation. However, most work focuses on inducing a single-dimension of positive sentiment using human annotated datasets, which limits the scale of the training dataset. In this paper, we propose to model various emotions in large unannotated conversations, such as joy, trust and anticipation, by leveraging a latent variable to control the emotional intention of the response. Our proposed emotion-eliciting-Conditional-Variational-AutoEncoder (EE-CVAE) model generates more diverse and emotionally-intelligent responses compared to single-dimension baseline models in human evaluation.",
}