Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Content Enhanced BERT-based Text-to-SQL Generation   We present a simple methods to leverage the table content for the BERT-based
model to solve the text-to-SQL problem. Based on the observation that some of
the table content match some words in question string and some of the table
header also match some words in question string, we encode two addition feature
vector for the deep model. Our methods also benefit the model inference in
testing time as the tables are almost the same in training and testing time. We
test our model on the WikiSQL dataset and outperform the BERT-based baseline by
3.7% in logic form and 3.7% in execution accuracy and achieve state-of-the-art.

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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: adap-org
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: math
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The categories of this paper are: q-alg
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys