  Deep learning approaches to semantic parsing require a large amount of
labeled data, but annotating complex logical forms is costly. Here, we propose
Syntactic Question Abstraction and Retrieval (SQAR), a method to build a neural
semantic parser that translates a natural language (NL) query to a SQL logical
form (LF) with less than 1,000 annotated examples. SQAR first retrieves a
logical pattern from the train data by computing the similarity between NL
queries and then grounds a lexical information on the retrieved pattern in
order to generate the final LF. We validate SQAR by training models using
various small subsets of WikiSQL train data achieving up to 4.9% higher LF
accuracy compared to the previous state-of-the-art models on WikiSQL test set.
We also show that by using query-similarity to retrieve logical pattern, SQAR
can leverage a paraphrasing dataset achieving up to 5.9% higher LF accuracy
compared to the case where SQAR is trained by using only WikiSQL data. In
contrast to a simple pattern classification approach, SQAR can generate unseen
logical patterns upon the addition of new examples without re-training the
model. We also discuss an ideal way to create cost efficient and robust train
datasets when the data distribution can be approximated under a data-hungry
setting.
