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.

本研究提出了一种名为 StructCBR 的结构化案例推理方法, 通过利用案例和候选文本之间的子树级相似性, 从而改善解码器决策。在处理未见架构的情况下，应用于 Text-to-SQL 模型的推理时适应方法中，相较于先前的方法，每次从不同的数据库中获得相同的性能提高。

文本转SQL解析器的推理时自适应结构化基于案例推理