  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.
