论文标题
零击的扎根适应性可执行语义解析
Grounded Adaptation for Zero-shot Executable Semantic Parsing
论文作者
论文摘要
我们提出了零摄像可执行语义解析（天哪）的扎根改编，以使现有的语义解析器适应新环境（例如新数据库模式）。天哪，天哪，将一个前向语义解析器与向后的话语发生器结合在一起，以合成新环境中的数据（例如话语和SQL查询），然后选择循环一致的示例以适应解析器。与通常在训练环境中综合未验证的示例的数据启发不同，天库在新环境中综合了其输入输出一致性的新环境中的示例。在蜘蛛，sparc和cosql零射击语义解析任务上，天哪，天哪，提高了基线解析器的逻辑形式和执行精度。我们的分析表明，天然气天然气工有天平的表现优于培训环境中的数据实践，绩效随仓库合成的数据的数量而增加，并且自行合矛盾对于成功适应的核心至关重要。
We propose Grounded Adaptation for Zero-shot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e.g. new database schemas). GAZP combines a forward semantic parser with a backward utterance generator to synthesize data (e.g. utterances and SQL queries) in the new environment, then selects cycle-consistent examples to adapt the parser. Unlike data-augmentation, which typically synthesizes unverified examples in the training environment, GAZP synthesizes examples in the new environment whose input-output consistency are verified. On the Spider, Sparc, and CoSQL zero-shot semantic parsing tasks, GAZP improves logical form and execution accuracy of the baseline parser. Our analyses show that GAZP outperforms data-augmentation in the training environment, performance increases with the amount of GAZP-synthesized data, and cycle-consistency is central to successful adaptation.
