The Natural Language to SQL (NL2SQL) technique is used to convert natural language queries into executable SQL statements. Typically, slot-filling is employed as a classification method for multi-task cases to achieve this goal. However, slot-filling can result in inaccurate SQL statement generation due to negative migration issues arising from different classification tasks. To overcome this limitation, this study introduces a new approach called Multi-Layer Expert Generate SQL (MLEG-SQL), which utilizes a dedicated multi-task hierarchical network. The lower layer of the network extracts semantic features of natural language statements, while the upper layer builds a specialized expert system for handling specific classification tasks. This hierarchical approach mitigates performance degradation resulting from different task conflicts. The proposed method was evaluated on the WiKSQL dataset and was found to be effective in generating accurate SQL statements.

翻译：自然语言转SQL（NL2SQL）技术用于将自然语言查询转换为可执行的SQL语句。通常，槽填充被用作多任务场景下的分类方法来实现这一目标。然而，由于不同分类任务引发的负迁移问题，槽填充可能导致SQL语句生成不准确。为克服这一局限性，本研究提出了一种称为多层专家生成SQL（MLEG-SQL）的新方法，该方法采用专用的多任务分层网络。网络下层提取自然语言语句的语义特征，而上层则构建用于处理特定分类任务的专用专家系统。这种分层方法能够缓解因不同任务冲突导致的性能下降问题。所提方法在WiKSQL数据集上进行了评估，结果表明其能够有效生成准确的SQL语句。