Papers › A Hybrid Convolutional Variational Autoencoder for Text Generation

A Hybrid Convolutional Variational Autoencoder for Text Generation

8 Feb 2017EMNLP 2017 9arXiv:1702.02390archive 2025-07-28

Stanislau Semeniuta, Aliaksei Severyn, Erhardt Barth

In this paper we explore the effect of architectural choices on learning a Variational Autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for text where both the encoder and decoder are RNNs, we propose a novel hybrid architecture that blends fully feed-forward convolutional and deconvolutional components with a recurrent language model. Our architecture exhibits several attractive properties such as faster run time and convergence, ability to better handle long sequences and, more importantly, it helps to avoid some of the major difficulties posed by training VAE models on textual data.

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