Syntactically Informed Text Compression with Recurrent Neural Networks
  We present a self-contained system for constructing natural language models for use in text compression. Our system improves upon previous neural network based models by utilizing recent advances in syntactic parsing -- Google's SyntaxNet -- to augment character-level recurrent neural networks. RNNs have proven exceptional in modeling sequence data such as text, as their architecture allows for modeling of long-term contextual information. 
David Cox
cs.LG cs.CL cs.IT math.IT