1 research outputs found
Extreme Model Compression for On-device Natural Language Understanding
In this paper, we propose and experiment with techniques for extreme
compression of neural natural language understanding (NLU) models, making them
suitable for execution on resource-constrained devices. We propose a
task-aware, end-to-end compression approach that performs word-embedding
compression jointly with NLU task learning. We show our results on a
large-scale, commercial NLU system trained on a varied set of intents with huge
vocabulary sizes. Our approach outperforms a range of baselines and achieves a
compression rate of 97.4% with less than 3.7% degradation in predictive
performance. Our analysis indicates that the signal from the downstream task is
important for effective compression with minimal degradation in performance.Comment: Long paper at COLING 202