论文标题
CQR-SQL：对话问题重新制定增强了与上下文有关的文本到SQL解析器
CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers
论文作者
论文摘要
与上下文有关的文本到SQL是将多转 - 问题转化为与数据库相关的SQL查询的任务。现有方法通常着重于充分利用历史上下文或以前预测的SQL进行SQL解析，同时忽略了明确理解模式和对话依赖性，例如共同参考，省略号和用户的重点更改。在本文中，我们提出了使用辅助对话问题重新印度（CQR）的CQR-SQL，以明确利用模式并将上下文依赖性用于SQL解析。具体而言，我们首先提出了一种增强的递归CQR方法，以产生与域相关的独立问题。其次，我们通过架构接地一致性任务和树结构的SQL解析一致性任务来训练CQR-SQL模型，以将多转化问题和辅助自共同问题的语义映射到相同的潜在空间中，从而增强了SQL通过充分的背景理解的能力。在撰写本文时，我们的CQR-SQL在两个与上下文依赖的文本到SQL基准SPARC和COSQL上实现了新的最新结果。
Context-dependent text-to-SQL is the task of translating multi-turn questions into database-related SQL queries. Existing methods typically focus on making full use of history context or previously predicted SQL for currently SQL parsing, while neglecting to explicitly comprehend the schema and conversational dependency, such as co-reference, ellipsis and user focus change. In this paper, we propose CQR-SQL, which uses auxiliary Conversational Question Reformulation (CQR) learning to explicitly exploit schema and decouple contextual dependency for SQL parsing. Specifically, we first present a schema enhanced recursive CQR method to produce domain-relevant self-contained questions. Secondly, we train CQR-SQL models to map the semantics of multi-turn questions and auxiliary self-contained questions into the same latent space through schema grounding consistency task and tree-structured SQL parsing consistency task, which enhances the abilities of SQL parsing by adequately contextual understanding. At the time of writing, our CQR-SQL achieves new state-of-the-art results on two context-dependent text-to-SQL benchmarks SParC and CoSQL.
