  Neural networks using numerous text data have been successfully applied to a
variety of tasks. While massive text data is usually compressed using
techniques such as grammar compression, almost all of the previous machine
learning methods assume already decompressed sequence data as their input. In
this paper, we propose a method to directly apply neural sequence models to
text data compressed with grammar compression algorithms without decompression.
To encode the unique symbols that appear in compression rules, we introduce
composer modules to incrementally encode the symbols into vector
representations. Through experiments on real datasets, we empirically showed
that the proposal model can achieve both memory and computational efficiency
while maintaining moderate performance.
