  Learning latent representations from long text sequences is an important
first step in many natural language processing applications. Recurrent Neural
Networks (RNNs) have become a cornerstone for this challenging task. However,
the quality of sentences during RNN-based decoding (reconstruction) decreases
with the length of the text. We propose a sequence-to-sequence, purely
convolutional and deconvolutional autoencoding framework that is free of the
above issue, while also being computationally efficient. The proposed method is
simple, easy to implement and can be leveraged as a building block for many
applications. We show empirically that compared to RNNs, our framework is
better at reconstructing and correcting long paragraphs. Quantitative
evaluation on semi-supervised text classification and summarization tasks
demonstrate the potential for better utilization of long unlabeled text data.
