  Deep learning models have become state of the art for natural language
processing (NLP) tasks, however deploying these models in production system
poses significant memory constraints. Existing compression methods are either
lossy or introduce significant latency. We propose a compression method that
leverages low rank matrix factorization during training,to compress the word
embedding layer which represents the size bottleneck for most NLP models. Our
models are trained, compressed and then further re-trained on the downstream
task to recover accuracy while maintaining the reduced size. Empirically, we
show that the proposed method can achieve 90% compression with minimal impact
in accuracy for sentence classification tasks, and outperforms alternative
methods like fixed-point quantization or offline word embedding compression. We
also analyze the inference time and storage space for our method through FLOP
calculations, showing that we can compress DNN models by a configurable ratio
and regain accuracy loss without introducing additional latency compared to
fixed point quantization. Finally, we introduce a novel learning rate schedule,
the Cyclically Annealed Learning Rate (CALR), which we empirically demonstrate
to outperform other popular adaptive learning rate algorithms on a sentence
classification benchmark.
