Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Synthesize Data for Semantic Parsing   Synthesizing data for semantic parsing has gained increasing attention
recently. However, most methods require handcrafted (high-precision) rules in
their generative process, hindering the exploration of diverse unseen data. In
this work, we propose a generative model which features a (non-neural) PCFG
that models the composition of programs (e.g., SQL), and a BART-based
translation model that maps a program to an utterance. Due to the simplicity of
PCFG and pre-trained BART, our generative model can be efficiently learned from
existing data at hand. Moreover, explicitly modeling compositions using PCFG
leads to a better exploration of unseen programs, thus generate more diverse
data. We evaluate our method in both in-domain and out-of-domain settings of
text-to-SQL parsing on the standard benchmarks of GeoQuery and Spider,
respectively. Our empirical results show that the synthesized data generated
from our model can substantially help a semantic parser achieve better
compositional and domain generalization.

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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: math
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas