Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Constrained Language Models Yield Few-Shot Semantic Parsers   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.

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The categories of this paper are: nlin
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an