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The task of generating a database query from a question in natural languagesuffers from ambiguity and insufficiently precise description of the goal. Theproblem is amplified when the system needs to generalize to databases unseen attraining. In this paper, we consider the case when, at the test time, thesystem has access to an external criterion that evaluates the generatedqueries. The criterion can vary from checking that a query executes withouterrors to verifying the query on a set of tests. In this setting, we augmentneural autoregressive models with a search algorithm that looks for a querysatisfying the criterion. We apply our approach to the state-of-the-artsemantic parsers and report that it allows us to find many queries passing allthe tests on different datasets.

Searching for Better Database Queries in the Outputs of Semantic Parsers