Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Potential of Lexico-logical Alignments for Semantic Parsing to
  SQL Queries   Large-scale semantic parsing datasets annotated with logical forms have
enabled major advances in supervised approaches. But can richer supervision
help even more? To explore the utility of fine-grained, lexical-level
supervision, we introduce Squall, a dataset that enriches 11,276
WikiTableQuestions English-language questions with manually created SQL
equivalents plus alignments between SQL and question fragments. Our annotation
enables new training possibilities for encoder-decoder models, including
approaches from machine translation previously precluded by the absence of
alignments. We propose and test two methods: (1) supervised attention; (2)
adopting an auxiliary objective of disambiguating references in the input
queries to table columns. In 5-fold cross validation, these strategies improve
over strong baselines by 4.4% execution accuracy. Oracle experiments suggest
that annotated alignments can support further accuracy gains of up to 23.9%.

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The categories of this paper are: ao-sci
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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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: quant-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph