Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning

Understand

Sequence-to-sequence (Seq2seq) models have played an important role in the recent success of various natural language processing methods, such as machine translation, text summarization, and speech recognition.

• However, current Seq2seq models have trouble preserving global latent information from a long sequence of words.
• Variational autoencoder (VAE) alleviates this problem by learning a continuous semantic space of the input sentence.
• However, it does not solve the problem completely.