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        <title>Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling</title>
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    <abstract>Recurrent Variational Autoencoder has been widely used for language modeling and text generation tasks. These models often face a difficult optimization problem, also known as KL vanishing, where the posterior easily collapses to the prior and model will ignore latent codes in generative tasks. To address this problem, we introduce an improved Variational Wasserstein Autoencoder (WAE) with Riemannian Normalizing Flow (RNF) for text modeling. The RNF transforms a latent variable into a space that respects the geometric characteristics of input space, which makes posterior impossible to collapse to the non-informative prior. The Wasserstein objective minimizes the distance between marginal distribution and the prior directly and therefore does not force the posterior to match the prior. Empirical experiments show that our model avoids KL vanishing over a range of datasets and has better performance in tasks such as language modeling, likelihood approximation, and text generation. Through a series of experiments and analysis over latent space, we show that our model learns latent distributions that respect latent space geometry and is able to generate sentences that are more diverse.</abstract>
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    <identifier type="doi">10.18653/v1/N19-1025</identifier>
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%0 Conference Proceedings
%T Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling
%A Wang, Prince Zizhuang
%A Wang, William Yang
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F wang-wang-2019-riemannian
%X Recurrent Variational Autoencoder has been widely used for language modeling and text generation tasks. These models often face a difficult optimization problem, also known as KL vanishing, where the posterior easily collapses to the prior and model will ignore latent codes in generative tasks. To address this problem, we introduce an improved Variational Wasserstein Autoencoder (WAE) with Riemannian Normalizing Flow (RNF) for text modeling. The RNF transforms a latent variable into a space that respects the geometric characteristics of input space, which makes posterior impossible to collapse to the non-informative prior. The Wasserstein objective minimizes the distance between marginal distribution and the prior directly and therefore does not force the posterior to match the prior. Empirical experiments show that our model avoids KL vanishing over a range of datasets and has better performance in tasks such as language modeling, likelihood approximation, and text generation. Through a series of experiments and analysis over latent space, we show that our model learns latent distributions that respect latent space geometry and is able to generate sentences that are more diverse.
%R 10.18653/v1/N19-1025
%U https://aclanthology.org/N19-1025
%U https://doi.org/10.18653/v1/N19-1025
%P 284-294

Markdown (Informal)

[Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling](https://aclanthology.org/N19-1025) (Wang & Wang, NAACL 2019)

• Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling (Wang & Wang, NAACL 2019)

ACL

• Prince Zizhuang Wang and William Yang Wang. 2019. Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 284–294, Minneapolis, Minnesota. Association for Computational Linguistics.