Papers by Jidong Zhai

2 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.
Joint Geometrical and Statistical Domain Adaptation for Cross-domain Code Vulnerability Detection (2023.emnlp-main)

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Challenge: Existing approaches to detect code vulnerability are limited by labeled training data on target domains.
Approach: They propose a cross-domain code vulnerability detection framework called MNCRI . they propose mutual nearest neighbor contrastive learning to align the source and target domains .
Outcome: The proposed framework outperforms state-of-the-art methods in cross-domain code vulnerability detection tasks.

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