Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation (D19-1)
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| Challenge: | Existing work assumes the Gaussian priors of the latent variable, which are incapable of representing complex latent variables effectively. |
| Approach: | They propose to use the Dirichlet distribution with flexible structures to characterize latent variables in place of the Gaussian priors. |
| Outcome: | The proposed model outperforms existing models on the dialogue generation task. |
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| Challenge: | Variational autoencoders suffer from the notorious degeneration problem, according to a new study . utterance drop regularization is an important feature of the hierarchical RNNs . |
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DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
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Variational Autoregressive Decoder for Neural Response Generation (D18-1)
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| Challenge: | Existing variational Bayesian models generate responses from a single latent variable, which is not sufficient to model high variability in responses. |
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Hierarchy Response Learning for Neural Conversation Generation (D19-1)
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| Challenge: | Neural conversation generation models can't perceive and express the intention effectively, causing dull and generic responses. |
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RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023.acl-long)
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| Challenge: | Existing approaches to personalized dialogue generation rely on dialogue data paired with user traits, profiles or persona description sentences. |
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Speculative Sampling in Variational Autoencoders for Dialogue Response Generation (2021.findings-emnlp)
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| Challenge: | Existing studies have tried to improve variational models but they fail to learn proper mappings. |
| Approach: | They propose to use a variable-based sampling technique to find the most probable one from redundantly sampled latent variables to tie up the variable with a given response. |
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Better Exploiting Latent Variables in Text Modeling (P19-1)
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| Challenge: | Consistent gains in performance on two datasets, Penn Treebank and Yahoo, indicate the generalizability of our method. |
| Approach: | They propose a method to exploit latent variables through hidden state averaging by sampling latent variable multiple times at a gradient step. |
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HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation (C18-1)
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Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation (2022.findings-emnlp)
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| Challenge: | Variational Auto-Encoder (VAE) has been widely adopted in text generation due to its ability to learn flexible representations. |
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Implicit Deep Latent Variable Models for Text Generation (D19-1)
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| Challenge: | Variational auto-encoders have been used for text generation but their representation power is limited due to two reasons. |
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