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Question Generation from SQL Queries Improves Neural Semantic Parsing
2018
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
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-ofthe-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
doi:10.18653/v1/d18-1188
dblp:conf/emnlp/GuoSTDYCCCZ18
fatcat:i3kwgeokkvccdooqvg3hdkky6q