Statistical Model Compression for Small-Footprint Natural Language Understanding
This work addresses the need for efficient NLU models on hardware-restricted devices and cloud systems, presenting an incremental improvement through complementary compression techniques.
The paper tackles the problem of compressing natural language understanding models for small-footprint applications, achieving a 14-fold reduction in memory usage with minimal impact on predictive performance.
In this paper we investigate statistical model compression applied to natural language understanding (NLU) models. Small-footprint NLU models are important for enabling offline systems on hardware restricted devices, and for decreasing on-demand model loading latency in cloud-based systems. To compress NLU models, we present two main techniques, parameter quantization and perfect feature hashing. These techniques are complementary to existing model pruning strategies such as L1 regularization. We performed experiments on a large scale NLU system. The results show that our approach achieves 14-fold reduction in memory usage compared to the original models with minimal predictive performance impact.