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- W4385571716 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." @default.
- W4385571716 created "2023-08-05" @default.
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- W4385571716 date "2023-01-01" @default.
- W4385571716 modified "2023-09-24" @default.
- W4385571716 title "Eliciting Rich Positive Emotions in Dialogue Generation" @default.
- W4385571716 doi "https://doi.org/10.18653/v1/2023.sicon-1.1" @default.
- W4385571716 hasPublicationYear "2023" @default.
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