Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AutoQA: From Databases To QA Semantic Parsers With Only Synthetic
  Training Data   We propose AutoQA, a methodology and toolkit to generate semantic parsers
that answer questions on databases, with no manual effort. Given a database
schema and its data, AutoQA automatically generates a large set of high-quality
questions for training that covers different database operations. It uses
automatic paraphrasing combined with template-based parsing to find alternative
expressions of an attribute in different parts of speech. It also uses a novel
filtered auto-paraphraser to generate correct paraphrases of entire sentences.
We apply AutoQA to the Schema2QA dataset and obtain an average logical form
accuracy of 62.9% when tested on natural questions, which is only 6.4% lower
than a model trained with expert natural language annotations and paraphrase
data collected from crowdworkers. To demonstrate the generality of AutoQA, we
also apply it to the Overnight dataset. AutoQA achieves 69.8% answer accuracy,
16.4% higher than the state-of-the-art zero-shot models and only 5.2% lower
than the same model trained with human data.

35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
2
The categories of this paper are: math