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

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


Abstract
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 encoderdecoder 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%.
Anthology ID:
2020.findings-emnlp.167
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1849–1864
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.167
DOI:
10.18653/v1/2020.findings-emnlp.167
Bibkey:
Cite (ACL):
Tianze Shi, Chen Zhao, Jordan Boyd-Graber, Hal Daumé III, and Lillian Lee. 2020. On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1849–1864, Online. Association for Computational Linguistics.
Cite (Informal):
On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries (Shi et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.167.pdf
Code
 tzshi/squall +  additional community code