Syntactically Informed Text Compression with Recurrent Neural Networks
This work addresses text compression for applications like data storage and transmission, but it is incremental as it builds on existing methods with a new integration.
The authors tackled the problem of text compression by developing a system that integrates syntactic parsing from Google's SyntaxNet with character-level recurrent neural networks, resulting in improved performance over previous neural network-based models.
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.