@inproceedings{guo-etal-2018-question,
title = "Question Generation from {SQL} Queries Improves Neural Semantic Parsing",
author = "Guo, Daya and
Sun, Yibo and
Tang, Duyu and
Duan, Nan and
Yin, Jian and
Chi, Hong and
Cao, James and
Chen, Peng and
Zhou, Ming",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1188",
doi = "10.18653/v1/D18-1188",
pages = "1597--1607",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Question Generation from SQL Queries Improves Neural Semantic Parsing
%A Guo, Daya
%A Sun, Yibo
%A Tang, Duyu
%A Duan, Nan
%A Yin, Jian
%A Chi, Hong
%A Cao, James
%A Chen, Peng
%A Zhou, Ming
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F guo-etal-2018-question
%X 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.
%R 10.18653/v1/D18-1188
%U https://aclanthology.org/D18-1188
%U https://doi.org/10.18653/v1/D18-1188
%P 1597-1607
Markdown (Informal)
[Question Generation from SQL Queries Improves Neural Semantic Parsing](https://aclanthology.org/D18-1188) (Guo et al., EMNLP 2018)
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.