Statistical Model Compression for Small-Footprint Natural Language Understanding

TL;DR

This paper introduces statistical model compression techniques, parameter quantization and perfect feature hashing, to significantly reduce the memory footprint of NLU models while maintaining performance.

Contribution

It presents two novel compression methods that complement existing pruning strategies, enabling efficient small-footprint NLU models for offline and cloud applications.

Findings

Achieves 14-fold reduction in memory usage

Maintains minimal impact on predictive performance

Applicable to large-scale NLU systems

Abstract

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.