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

Understand

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.