  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. Our results
demonstrate that with only a small amount of data and very little code to
convert into English-like representations, our blueprint for rapidly
bootstrapping semantic parsers leads to surprisingly effective performance on
multiple community tasks, greatly exceeding baseline methods also trained on
the same limited data.
