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On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries

Tianze Shi, Chen Zhao, Jordan Boyd-Graber, Hal Daumé III, and Lillian Lee

In Findings of EMNLP (2020)

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%.

[pdf] [code] [arXiv]


    title = "On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries",
    author = "Shi, Tianze  and
    Zhao, Chen  and
    Boyd-Graber, Jordan  and
    Daum{\'e} III, Hal  and
    Lee, Lillian",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.167",
    pages = "1849--1864",

Tianze Shi @ Cornell University. Built with jekyll