Small Statistical Models by Random Feature Mixing
Abstract
The application of statistical NLP systems to
resource constrained devices is limited by the
need to maintain parameters for a large number of features and an alphabet mapping features to parameters. We introduce random
feature mixing to eliminate alphabet storage
and reduce the number of parameters without
severely impacting model performance.