Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Neural Data Synthesis for Semantic Parsing   Semantic parsing datasets are expensive to collect. Moreover, even the
questions pertinent to a given domain, which are the input of a semantic
parsing system, might not be readily available, especially in cross-domain
semantic parsing. This makes data augmentation even more challenging. Existing
methods to synthesize new data use hand-crafted or induced rules, requiring
substantial engineering effort and linguistic expertise to achieve good
coverage and precision, which limits the scalability. In this work, we propose
a purely neural approach of data augmentation for semantic parsing that
completely removes the need for grammar engineering while achieving higher
semantic parsing accuracy. Furthermore, our method can synthesize in the
zero-shot setting, where only a new domain schema is available without any
input-output examples of the new domain. On the Spider cross-domain text-to-SQL
semantic parsing benchmark, we achieve the state-of-the-art performance on the
development set (77.2% accuracy) using our zero-shot augmentation.

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The categories of this paper are: nucl-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
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The categories of this paper are: chem-ph