Byte-Level Recursive Convolutional Auto-Encoder for Text
This addresses scalable and homogeneous text generation for natural language processing, though it appears incremental as it builds on existing auto-encoder and convolutional methods.
The paper tackled the problem of non-sequential text generation at byte-level using a recursive convolutional auto-encoder, achieving better auto-encoding results than recurrent networks on large-scale datasets in multiple languages.
This article proposes to auto-encode text at byte-level using convolutional networks with a recursive architecture. The motivation is to explore whether it is possible to have scalable and homogeneous text generation at byte-level in a non-sequential fashion through the simple task of auto-encoding. We show that non-sequential text generation from a fixed-length representation is not only possible, but also achieved much better auto-encoding results than recurrent networks. The proposed model is a multi-stage deep convolutional encoder-decoder framework using residual connections, containing up to 160 parameterized layers. Each encoder or decoder contains a shared group of modules that consists of either pooling or upsampling layers, making the network recursive in terms of abstraction levels in representation. Results for 6 large-scale paragraph datasets are reported, in 3 languages including Arabic, Chinese and English. Analyses are conducted to study several properties of the proposed model.