论文标题
Hie-SQL：历史信息增强的网络，用于上下文依赖于文本到SQL语义解析
HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing
论文作者
论文摘要
最近，与上下文相关的文本到SQL语义解析，将自然语言转化为互动过程中的SQL引起了很多关注。以前的作品从交互历史语音或以前的预测SQL查询中利用上下文依赖性信息，但由于自然语言和逻辑形式SQL之间的不匹配，因此无法利用这两者。在这项工作中，我们提出了一个历史信息增强的文本到SQL模型（HIE-SQL），以利用历史话语和最后一个预测的SQL查询来利用上下文依赖性信息。鉴于不匹配，我们将自然语言和SQL视为两种方式，并提出了双峰预训练的模型，以弥合它们之间的差距。此外，我们设计了一个挂接图形，以增强从语音和SQL查询到数据库架构的连接。我们显示了我们的历史信息增强的方法通过大幅度的余量提高了HIE-SQL的性能，这在写作时在两个依赖上下文依赖的文本到SQL基准（SPARC和COSQL数据集）上取得了新的最新结果。
Recently, context-dependent text-to-SQL semantic parsing which translates natural language into SQL in an interaction process has attracted a lot of attention. Previous works leverage context-dependence information either from interaction history utterances or the previous predicted SQL queries but fail in taking advantage of both since of the mismatch between natural language and logic-form SQL. In this work, we propose a History Information Enhanced text-to-SQL model (HIE-SQL) to exploit context-dependence information from both history utterances and the last predicted SQL query. In view of the mismatch, we treat natural language and SQL as two modalities and propose a bimodal pre-trained model to bridge the gap between them. Besides, we design a schema-linking graph to enhance connections from utterances and the SQL query to the database schema. We show our history information enhanced methods improve the performance of HIE-SQL by a significant margin, which achieves new state-of-the-art results on the two context-dependent text-to-SQL benchmarks, the SparC and CoSQL datasets, at the writing time.
