Unsupervised Document Embedding With CNNs

Understand

We propose a new model for unsupervised document embedding.

• Leading existing approaches either require complex inference or use recurrent neural networks (RNN) that are difficult to parallelize.
• We take a different route and develop a convolutional neural network (CNN) embedding model.
• Our CNN architecture is fully parallelizable resulting in over 10x speedup in inference time over RNN models.