  A bottleneck to developing Semantic Parsing (SP) models is the need for a
large volume of human-labeled training data. Given the complexity and cost of
human annotation for SP, labeled data is often scarce, particularly in
multilingual settings. Large Language Models (LLMs) excel at SP given only a
few examples, however LLMs are unsuitable for runtime systems which require low
latency. In this work, we propose CLASP, a simple method to improve
low-resource SP for moderate-sized models: we generate synthetic data from
AlexaTM 20B to augment the training set for a model 40x smaller (500M
parameters). We evaluate on two datasets in low-resource settings: English
PIZZA, containing either 348 or 16 real examples, and mTOP cross-lingual
zero-shot, where training data is available only in English, and the model must
generalize to four new languages. On both datasets, we show significant
improvements over strong baseline methods.
