Conventional text-to-SQL parsers are not good at synthesizing complex SQL
queries that involve multiple tables or columns, due to the challenges inherent
in identifying the correct schema items and performing accurate alignment
between question and schema items. To address the above issue, we present a
schema-aware multi-task learning framework (named MTSQL) for complicated SQL
queries. Specifically, we design a schema linking discriminator module to
distinguish the valid question-schema linkings, which explicitly instructs the
encoder by distinctive linking relations to enhance the alignment quality. On
the decoder side, we define 6-type relationships to describe the connections
between tables and columns (e.g., WHERE_TC), and introduce an operator-centric
triple extractor to recognize those associated schema items with the predefined
relationship. Also, we establish a rule set of grammar constraints via the
predicted triples to filter the proper SQL operators and schema items during
the SQL generation. On Spider, a cross-domain challenging text-to-SQL
benchmark, experimental results indicate that MTSQL is more effective than
baselines, especially in extremely hard scenarios. Moreover, further analyses
verify that our approach leads to promising improvements for complicated SQL
queries.

传统的文本到 SQL 解析器在合成涉及多个表格或列的复杂 SQL 查询方面表现不佳，为了解决这个问题，我们提出了一种面向模式的多任务学习框架（名为 MTSQL）来处理复杂的 SQL 查询。通过设计一个模式链接鉴别器模块来区分有效的问题 - 模式关联，明确指示编码器通过特殊的链接关系来增强对齐质量。在解码器方面，我们定义了 6 种关系类型来描述表格和列之间的连接，并引入以运算符为中心的三元抽取器来识别那些与预定义关系相关的模式项。此外，我们通过预测的三元组建立了一组语法约束规则集，用于在 SQL 生成过程中过滤适当的 SQL 运算符和模式项。在跨领域的具有挑战性的文本到 SQL 基准测试 Spider 上，实验结果表明 MTSQL 比基线方法更有效，特别是在极端困难的场景中。此外，进一步的分析验证了我们的方法在处理复杂 SQL 查询方面的有希望的改进。