  The current state-of-the-art task-oriented semantic parsing models use BERT
or RoBERTa as pretrained encoders; these models have huge memory footprints.
This poses a challenge to their deployment for voice assistants such as Amazon
Alexa and Google Assistant on edge devices with limited memory budgets. We
propose to learn compositional code embeddings to greatly reduce the sizes of
BERT-base and RoBERTa-base. We also apply the technique to DistilBERT,
ALBERT-base, and ALBERT-large, three already compressed BERT variants which
attain similar state-of-the-art performances on semantic parsing with much
smaller model sizes. We observe 95.15% ~ 98.46% embedding compression rates and
20.47% ~ 34.22% encoder compression rates, while preserving greater than 97.5%
semantic parsing performances. We provide the recipe for training and analyze
the trade-off between code embedding sizes and downstream performances.
