  We explore the use of large pretrained language models as few-shot semantic
parsers. The goal in semantic parsing is to generate a structured meaning
representation given a natural language input. However, language models are
trained to generate natural language. To bridge the gap, we use language models
to paraphrase inputs into a controlled sublanguage resembling English that can
be automatically mapped to a target meaning representation. With a small amount
of data and very little code to convert into English-like representations, we
provide a blueprint for rapidly bootstrapping semantic parsers and demonstrate
good performance on multiple tasks.
