Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recent Advances in SQL Query Generation: A Survey   Natural language is hypothetically the best user interface for many domains.
However, general models that provide an interface between natural language and
any other domain still do not exist. Providing natural language interface to
relational databases could possibly attract a vast majority of users that are
or are not proficient with query languages. With the rise of deep learning
techniques, there is extensive ongoing research in designing a suitable natural
language interface to relational databases.
  This survey aims to overview some of the latest methods and models proposed
in the area of SQL query generation from natural language. We describe models
with various architectures such as convolutional neural networks, recurrent
neural networks, pointer networks, reinforcement learning, etc. Several
datasets intended to address the problem of SQL query generation are
interpreted and briefly overviewed. In the end, evaluation metrics utilized in
the field are presented mainly as a combination of execution accuracy and
logical form accuracy.

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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: math
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The categories of this paper are: hep-th
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: econ