Question Generation from SQL Queries Improves Neural Semantic Parsing
Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Ping Yin, Hong Chi, James Cao, Peng Chen, Ming Zhou · 2018
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 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.