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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.findings-emnlp.167.pdf
- Code
- tzshi/squall + additional community code