  Generating queries corresponding to natural language questions is a long
standing problem. Traditional methods lack language flexibility, while newer
sequence-to-sequence models require large amount of data. Schema-agnostic
sequence-to-sequence models can be fine-tuned for a specific schema using a
small dataset but these models have relatively low accuracy. We present a
method that transforms the query generation problem into an intent
classification and slot filling problem. This method can work using small
datasets. For questions similar to the ones in the training dataset, it
produces complex queries with high accuracy. For other questions, it can use a
template-based approach or predict query pieces to construct the queries, still
at a higher accuracy than sequence-to-sequence models. On a real-world dataset,
a schema fine-tuned state-of-the-art generative model had 60\% exact match
accuracy for the query generation task, while our method resulted in 92\% exact
match accuracy.
