Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Did You Ask a Good Question? A Cross-Domain Question Intention
  Classification Benchmark for Text-to-SQL   Neural models have achieved significant results on the text-to-SQL task, in
which most current work assumes all the input questions are legal and generates
a SQL query for any input. However, in the real scenario, users can input any
text that may not be able to be answered by a SQL query. In this work, we
propose TriageSQL, the first cross-domain text-to-SQL question intention
classification benchmark that requires models to distinguish four types of
unanswerable questions from answerable questions. The baseline RoBERTa model
achieves a 60% F1 score on the test set, demonstrating the need for further
improvement on this task. Our dataset is available at
https://github.com/chatc/TriageSQL.

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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: nlin
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom