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- W4378465303 abstract "Conditional variational autoencoders (CVAEs) have been used recently for diverse response generation, by introducing latent variables to represent the relationship between a dialog context and its potential responses. However, the diversity of the generated responses brought by a CVAE model is limited due to the oversimplified assumption of the isotropic Gaussian prior. We propose, Dior-CVAE, a hierarchical CVAE model with an informative prior produced by a diffusion model. Dior-CVAE derives a series of layer-wise latent variables using attention mechanism and infusing them into decoder layers accordingly. We propose memory dropout in the latent infusion to alleviate posterior collapse. The prior distribution of the latent variables is parameterized by a diffusion model to introduce a multimodal distribution. Overall, experiments on two popular open-domain dialog datasets indicate the advantages of our approach over previous Transformer-based variational dialog models in dialog response generation. We publicly release the code for reproducing Dior-CVAE and all baselines at https://github.com/SkyFishMoon/Latent-Diffusion-Response-Generation." @default.
- W4378465303 created "2023-05-27" @default.
- W4378465303 creator A5027450194 @default.
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- W4378465303 date "2023-05-24" @default.
- W4378465303 modified "2023-09-25" @default.
- W4378465303 title "Dior-CVAE: Diffusion Priors in Variational Dialog Generation" @default.
- W4378465303 doi "https://doi.org/10.48550/arxiv.2305.15025" @default.
- W4378465303 hasPublicationYear "2023" @default.
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