Question Generation from SQL Queries Improves Neural Semantic Parsing

Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, Ming Zhou


Abstract
In this paper, we study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data. We conduct our study on WikiSQL, the largest hand-annotated semantic parsing dataset to date. First, we demonstrate that question generation is an effective method that empowers us to learn a state-of-the-art neural network based semantic parser with thirty percent of the supervised training data. Second, we show that applying question generation to the full supervised training data further improves the state-of-the-art model. In addition, we observe that there is a logarithmic relationship between the accuracy of a semantic parser and the amount of training data.
Anthology ID:
D18-1188
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1597–1607
Language:
URL:
https://aclanthology.org/D18-1188
DOI:
10.18653/v1/D18-1188
Bibkey:
Cite (ACL):
Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, and Ming Zhou. 2018. Question Generation from SQL Queries Improves Neural Semantic Parsing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1597–1607, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Question Generation from SQL Queries Improves Neural Semantic Parsing (Guo et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1188.pdf
Data
WikiSQLWikiTableQuestions