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arXiv · 2004.10603

Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck

Yang Zhao·Ping Yu·Suchismit Mahapatra·Qinliang Su·Changyou Chen
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Abstract

Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. However, the sequential text generation common pitfall with VAEs is that the model tends to ignore latent variables with a strong auto-regressive decoder. In this paper, we propose a principled approach to alleviate this issue by applying a discretized bottleneck to enforce an implicit latent feature matching in a more compact latent space. We impose a shared discrete latent space where each input is learned to choose a combination of latent atoms as a regularized latent representation. Our model endows a promising capability to model underlying semantics of discrete sequences and thus provide more interpretative latent structures. Empirically, we demonstrate our model's efficiency and effectiveness on a broad range of tasks, including language modeling, unaligned text style transfer, dialog response generation, and neural machine translation.

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Yang Zhao, Ping Yu, Suchismit Mahapatra, Qinliang Su, Changyou Chen. 2020-04-22. Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck. https://arxiv.org/abs/2004.10603

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