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• W2890940245 abstract "Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation. However, existing CVAE-based models often generate responses from a single latent variable which may not be sufficient to model high variability in responses. To solve this problem, we propose a novel model that sequentially introduces a series of latent variables to condition the generation of each word in the response sequence. In addition, the approximate posteriors of these latent variables are augmented with a backward Recurrent Neural Network (RNN), which allows the latent variables to capture long-term dependencies of future tokens in generation. To facilitate training, we supplement our model with an auxiliary objective that predicts the subsequent bag of words. Empirical experiments conducted on Opensubtitle and Reddit datasets show that the proposed model leads to significant improvement on both relevance and diversity over state-of-the-art baselines." @default.
• W2890940245 created "2018-09-27" @default.
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• W2890940245 date "2018-01-01" @default.
• W2890940245 modified "2023-10-16" @default.
• W2890940245 title "Variational Autoregressive Decoder for Neural Response Generation" @default.
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• W2890940245 doi "https://doi.org/10.18653/v1/d18-1354" @default.
• W2890940245 hasPublicationYear "2018" @default.
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