Deconvolutional Paragraph Representation Learning

Abstract

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.

Accepted by NIPS 2017

References in corpus (5)

• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• A Convolutional Neural Network for Modelling Sentences
• A Hierarchical Neural Autoencoder for Paragraphs and Documents
• Encoding Source Language with Convolutional Neural Network for Machine Translation
• A Hybrid Convolutional Variational Autoencoder for Text Generation

Cited by in corpus (2)

• Adversarial Symmetric Variational Autoencoder
• Improving Visually Grounded Sentence Representations with Self-Attention