  Inference-time adaptation methods for semantic parsing are useful for
leveraging examples from newly-observed domains without repeated fine-tuning.
Existing approaches typically bias the decoder by simply concatenating
input-output example pairs (cases) from the new domain at the encoder's input
in a Seq-to-Seq model. Such methods cannot adequately leverage the structure of
logical forms in the case examples. We propose StructCBR, a structured
case-based reasoning approach, which leverages subtree-level similarity between
logical forms of cases and candidate outputs, resulting in better decoder
decisions. For the task of adapting Text-to-SQL models to unseen schemas, we
show that exploiting case examples in a structured manner via StructCBR offers
consistent performance improvements over prior inference-time adaptation
methods across five different databases. To the best of our knowledge, we are
the first to attempt inference-time adaptation of Text-to-SQL models, and
harness trainable structured similarity between subqueries.
