Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semantic Parsing with Dual Learning   Semantic parsing converts natural language queries into structured logical
forms. The paucity of annotated training samples is a fundamental challenge in
this field. In this work, we develop a semantic parsing framework with the dual
learning algorithm, which enables a semantic parser to make full use of data
(labeled and even unlabeled) through a dual-learning game. This game between a
primal model (semantic parsing) and a dual model (logical form to query) forces
them to regularize each other, and can achieve feedback signals from some
prior-knowledge. By utilizing the prior-knowledge of logical form structures,
we propose a novel reward signal at the surface and semantic levels which tends
to generate complete and reasonable logical forms. Experimental results show
that our approach achieves new state-of-the-art performance on ATIS dataset and
gets competitive performance on Overnight dataset.

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The categories of this paper are: astro-ph
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: eess