  Text-to-SQL task maps natural language utterances to structured queries that
can be issued to a database. State-of-the-art (SOTA) systems rely on finetuning
large, pre-trained language models in conjunction with constrained decoding
applying a SQL parser. On the well established Spider dataset, we begin with
Oracle studies: specifically, choosing an Oracle hypothesis from a SOTA model's
10-best list, yields a $7.7\%$ absolute improvement in both exact match (EM)
and execution (EX) accuracy, showing significant potential improvements with
reranking. Identifying coherence and correctness as reranking approaches, we
design a model generating a query plan and propose a heuristic schema linking
algorithm. Combining both approaches, with T5-Large, we obtain a consistent
$1\% $ improvement in EM accuracy, and a $~2.5\%$ improvement in EX,
establishing a new SOTA for this task. Our comprehensive error studies on DEV
data show the underlying difficulty in making progress on this task.

Computation and Language;Artificial Intelligence