@inproceedings{li-etal-2019-stable,
title = "A Stable Variational Autoencoder for Text Modelling",
author = "Li, Ruizhe and
Li, Xiao and
Lin, Chenghua and
Collinson, Matthew and
Mao, Rui",
editor = "van Deemter, Kees and
Lin, Chenghua and
Takamura, Hiroya",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
month = oct # "{--}" # nov,
year = "2019",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-8673",
doi = "10.18653/v1/W19-8673",
pages = "594--599",
abstract = "Variational Autoencoder (VAE) is a powerful method for learning representations of high-dimensional data. However, VAEs can suffer from an issue known as latent variable collapse (or KL term vanishing), where the posterior collapses to the prior and the model will ignore the latent codes in generative tasks. Such an issue is particularly prevalent when employing VAE-RNN architectures for text modelling (Bowman et al., 2016; Yang et al., 2017). In this paper, we present a new architecture called Full-Sampling-VAE-RNN, which can effectively avoid latent variable collapse. Compared to the general VAE-RNN architectures, we show that our model can achieve much more stable training process and can generate text with significantly better quality.",
}
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<abstract>Variational Autoencoder (VAE) is a powerful method for learning representations of high-dimensional data. However, VAEs can suffer from an issue known as latent variable collapse (or KL term vanishing), where the posterior collapses to the prior and the model will ignore the latent codes in generative tasks. Such an issue is particularly prevalent when employing VAE-RNN architectures for text modelling (Bowman et al., 2016; Yang et al., 2017). In this paper, we present a new architecture called Full-Sampling-VAE-RNN, which can effectively avoid latent variable collapse. Compared to the general VAE-RNN architectures, we show that our model can achieve much more stable training process and can generate text with significantly better quality.</abstract>
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%0 Conference Proceedings
%T A Stable Variational Autoencoder for Text Modelling
%A Li, Ruizhe
%A Li, Xiao
%A Lin, Chenghua
%A Collinson, Matthew
%A Mao, Rui
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F li-etal-2019-stable
%X Variational Autoencoder (VAE) is a powerful method for learning representations of high-dimensional data. However, VAEs can suffer from an issue known as latent variable collapse (or KL term vanishing), where the posterior collapses to the prior and the model will ignore the latent codes in generative tasks. Such an issue is particularly prevalent when employing VAE-RNN architectures for text modelling (Bowman et al., 2016; Yang et al., 2017). In this paper, we present a new architecture called Full-Sampling-VAE-RNN, which can effectively avoid latent variable collapse. Compared to the general VAE-RNN architectures, we show that our model can achieve much more stable training process and can generate text with significantly better quality.
%R 10.18653/v1/W19-8673
%U https://aclanthology.org/W19-8673
%U https://doi.org/10.18653/v1/W19-8673
%P 594-599
Markdown (Informal)
[A Stable Variational Autoencoder for Text Modelling](https://aclanthology.org/W19-8673) (Li et al., INLG 2019)
ACL
- Ruizhe Li, Xiao Li, Chenghua Lin, Matthew Collinson, and Rui Mao. 2019. A Stable Variational Autoencoder for Text Modelling. In Proceedings of the 12th International Conference on Natural Language Generation, pages 594–599, Tokyo, Japan. Association for Computational Linguistics.