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• W3014185253 abstract "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." @default.
• W3014185253 created "2020-04-10" @default.
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• W3014185253 date "2020-03-31" @default.
• W3014185253 modified "2023-10-16" @default.
• W3014185253 title "SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Bases" @default.
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• W3014185253 doi "https://doi.org/10.48550/arxiv.2003.13956" @default.
• W3014185253 hasPublicationYear "2020" @default.
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