  Semantic parsing is an important NLP problem, particularly for voice
assistants such as Alexa and Google Assistant. State-of-the-art (SOTA) semantic
parsers are seq2seq architectures based on large language models that have been
pretrained on vast amounts of text. To better leverage that pretraining, recent
work has explored a reformulation of semantic parsing whereby the output
sequences are themselves natural language sentences, but in a controlled
fragment of natural language. This approach delivers strong results,
particularly for few-shot semantic parsing, which is of key importance in
practice and the focus of our paper. We push this line of work forward by
introducing an automated methodology that delivers very significant additional
improvements by utilizing modest amounts of unannotated data, which is
typically easy to obtain. Our method is based on a novel synthesis of four
techniques: joint training with auxiliary unsupervised tasks; constrained
decoding; self-training; and paraphrasing. We show that this method delivers
new SOTA few-shot performance on the Overnight dataset, particularly in very
low-resource settings, and very compelling few-shot results on a new semantic
parsing dataset.
