Translating Questions to SQL Queries with Generative Parsers Discriminatively Reranked

In this paper, we define models for automatically translating a factoid question in natural language to an SQL query that retrieves the correct answer from a target relational database (DB). We exploit the DB structure to generate a set of candidate SQL queries, which we rerank with an SVM-ranker based on tree kernels. In particular, in the generation phase, we use (i) lexical dependencies in the question and (ii) the DB metadata, to build a set of plausible SELECT, WHERE and FROM clauses enriched with meaningful joins. We combine the clauses by means of rules and a heuristic weighting scheme, which allows for generating a ranked list of candidate SQL queries. This approach can be recursively applied to deal with complex questions, requiring nested SELECT instructions. Finally, we apply the reranker to reorder the list of question and SQL candidate pairs, whose members are represented as syntactic trees. The F1 of our model derived on standard benchmarks, 87% on the first question, is in line with the best models using external and expensive hand-crafted resources such as the question meaning interpretation. Moreover, our system shows a Recall of the correct answer of about 94% and 98% on the first 2 and 5 candidates, respectively. This is an interesting outcome considering that we only need pairs of questions and answers concerning a target DB (no SQL query is needed) to train our model.