  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.
