论文标题
SPARQA：基于骨架的语义解析，用于知识库的复杂问题
SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Bases
论文作者
论文摘要
语义解析将自然语言问题转变为知识库的正式查询。许多现有的方法依赖于句法解析，例如依赖性。但是，产生这种表达性形式主义的准确性并不令人满意。在本文中，我们提出了一种新颖的骨骼语法，以代表复杂问题的高级结构。这种具有基于BERT的解析算法的专用粗粒形式主义有助于提高下游细粒语义解析的准确性。此外，为了使问题的结构与知识基础的结构保持一致，我们的多策略方法结合了句子级别和单词级别的语义。我们的方法在几个数据集上显示出有希望的性能。
Semantic parsing transforms a natural language question into a formal query over a knowledge base. Many existing methods rely on syntactic parsing like dependencies. However, the accuracy of producing such expressive formalisms is not satisfying on long complex questions. In this paper, we propose a novel skeleton grammar to represent the high-level structure of a complex question. This dedicated coarse-grained formalism with a BERT-based parsing algorithm helps to improve the accuracy of the downstream fine-grained semantic parsing. Besides, to align the structure of a question with the structure of a knowledge base, our multi-strategy method combines sentence-level and word-level semantics. Our approach shows promising performance on several datasets.
