论文标题
GraphQ ir：用一个中间表示形式统一图形查询语言的语义解析
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation
论文作者
论文摘要
在天然语言和形式语言之间存在巨大的语义差距的前提下，神经语义解析通常是由于其处理输入语义和输出语法的复杂性所瓶颈而瓶颈。最近的作品提出了几种形式的补充监督，但没有一种跨多种形式的语言概括。本文提出了一个名为GraphQ IR的图形查询语言的统一中间表示（IR）。它具有类似自然的语言表达式，可以桥接语义差距和正式定义的语法，以维持图形结构。因此，神经语义解析器可以更精确地将用户查询转换为GraphQ IR，以后可以将其无效地编译成各种下游图形查询语言。在标准I.I.D.，分布外和低资源设置的标准I.I.D.中，包括KQA Pro，Onetight GrailQA和Metaqa-Cypher在内的多个基准测试的广泛实验以及最高可提高了11％精度的低资源设置。
Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax. Recent works have proposed several forms of supplementary supervision but none is generalized across multiple formal languages. This paper proposes a unified intermediate representation (IR) for graph query languages, named GraphQ IR. It has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. Therefore, a neural semantic parser can more precisely convert user queries into GraphQ IR, which can be later losslessly compiled into various downstream graph query languages. Extensive experiments on several benchmarks including KQA Pro, Overnight, GrailQA, and MetaQA-Cypher under standard i.i.d., out-of-distribution, and low-resource settings validate GraphQ IR's superiority over the previous state-of-the-arts with a maximum 11% accuracy improvement.
