论文标题
IGSQL：基于数据库架构相互作用图的神经模型，用于上下文依赖性文本到SQL生成
IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation
论文作者
论文摘要
近年来，与上下文有关的文本到SQL任务引起了很多关注。以前与上下文相关的文本到SQL任务的模型仅集中于利用历史用户输入。在这项工作中，除了使用编码器捕获用户输入的历史信息外，我们还建议一个数据库架构交互图编码器来利用数据库架构项目的历史信息。在解码阶段，我们引入了一种栅极机制来权衡不同词汇的重要性，然后对SQL令牌进行预测。我们在基准SPARC和COSQL数据集上评估了我们的模型，这些数据集是两个大型复杂上下文依赖的跨域文本到SQL数据集。我们的模型的表现优于先前的最先进模型，并在两个数据集中实现了新的最新结果。比较和消融结果证明了我们的模型的功效以及数据库架构相互作用图编码器的实用性。
Context-dependent text-to-SQL task has drawn much attention in recent years. Previous models on context-dependent text-to-SQL task only concentrate on utilizing historical user inputs. In this work, in addition to using encoders to capture historical information of user inputs, we propose a database schema interaction graph encoder to utilize historicalal information of database schema items. In decoding phase, we introduce a gate mechanism to weigh the importance of different vocabularies and then make the prediction of SQL tokens. We evaluate our model on the benchmark SParC and CoSQL datasets, which are two large complex context-dependent cross-domain text-to-SQL datasets. Our model outperforms previous state-of-the-art model by a large margin and achieves new state-of-the-art results on the two datasets. The comparison and ablation results demonstrate the efficacy of our model and the usefulness of the database schema interaction graph encoder.
