LGESQL：混合局部和非局部关系的线图增强文本到SQL模型
LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations
摘要：
这项工作旨在解决文本到SQL任务中具有挑战性的异构图编码问题。以前的方法通常是以节点为中心的，并且仅仅利用不同的权重矩阵来参数化边类型，这1）忽略了嵌入在边的拓扑结构中的丰富语义，以及2）无法区分每个节点的局部和非局部关系。为此，我们提出了一个线图增强的文本到SQL（LGESQL）模型挖掘底层的关系功能，而不构造元路径。借助于线图，消息不仅通过节点之间的连接，而且通过有向边的拓扑更有效地传播。此外，在图迭代过程中，局部和非局部关系被区别地集成。我们还设计了一个辅助任务，称为图修剪，以提高鉴别能力的编码器。在撰写本文时，我们的框架在跨域文本到SQL基准测试Spider上实现了最先进的结果（Glove为62.8%，Electra为72.0%）。
英文摘要：
This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task. Previous methods are typically node-centric and merely utilize different weight matrices to parameterize edge types, which 1) ignore the rich semantics embedded in the topological structure of edges, and 2) fail to distinguish local and non-local relations for each node. To this end, we propose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying relational features without constructing meta-paths. By virtue of the line graph, messages propagate more efficiently through not only connections between nodes, but also the topology of directed edges. Furthermore, both local and non-local relations are integrated distinctively during the graph iteration. We also design an auxiliary task called graph pruning to improve the discriminative capability of the encoder. Our framework achieves state-of-the-art results (62.8% with Glove, 72.0% with Electra) on the cross-domain text-to-SQL benchmark Spider at the time of writing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
