Papers by Jiuding Sun

1 papers
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation (2022.emnlp-main)

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Challenge: Existing approaches to neural semantic parsing are limited by the semantic gap between natural and formal languages.
Approach: They propose a unified intermediate representation for graph query languages, named GraphQ IR, which has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure.
Outcome: The proposed representation can convert user queries into graphQ IR, which can later be losslessly compiled into various downstream graph query languages.

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